This paper analyzes the arbitrage efficiency of the first Chinese 50ETF option contracts traded on the Shanghai Stock Exchange (SSE). Put-call parity arbitrage, box spread arbitrage, and boundary arbitrage strategies are used to test the efficiency of the Chinese SSE 50ETF options market during the sample period (February 2015–April 2017). Furthermore, various transaction costs – such as transaction fees, market-impact costs, bid-ask spreads, margins and capital opportunity costs – and different scenarios are included in the arbitrage tests. The empirical results reveal that arbitrage opportunities are existing but infrequent when transaction costs are considered. In terms of box spread arbitrage and boundary arbitrage strategies, the percentage of profitable arbitrage opportunities is low. In terms of the put-call parity arbitrage strategy, the results held most of the time, and profitable arbitrage opportunities appear at times of great fluctuation in the Chinese stock market from May to September 2015. Further analysis of arbitrage opportunities in terms of both option moneyness and maturity show that more arbitrage opportunities appear in options with comparatively poor liquidity and inactive trading. Overall, there is no strong evidence found against the Chinese SSE 50ETF options market, although arbitrage opportunities exist infrequently.

Recent years, the popularity of wind power and the widely use of diversified loads have increased the uncertainty of power systems in both supply and demand sides. This paper develops a multi-Time scale unit commitment optimization model under wind power and future load uncertainties. First, dayahead wind power and electric load forecast is obtained by long short-Term memory network, based on which the on/off status and first-period output of units are determined. Then rolling economic dispatch is applied when real time data is collected from the system. To solve the above unit commitment and economic dispatch model, an improved particle swarm optimization algorithm is proposed. Finally, several experiment were performed to demonstrate the effectiveness of this research.

Index tracking is an passive strategy in portfolio management, it mimics the performance of a benchmark index to construct portfolios for obtaining the average return of the target market. Index tracking has become popular in investors because it possesses the advantages of low cost, high liquidity and lower risk. This paper introduced sensitivity analysis to construct a fuzzy multi-objective index tracking portfolio model with value at risk (SA-IT-VAR-FMOPM) when return rate was set as parabolic fuzzy variable, the sensitivity and VaR factors were considered in the model. An improved particle swarm optimization (IPSO) algorithm was used to search optimal solution for multi-objective problem. To verify the effective of the proposed model, Dow 30 index data were selected to the empirical experiment, the results show the fuzzy multi-objective index tracking portfolio model which considered the sensitivity and VaR factors can obtain more stable portfolio and achieve the average return of target market.

An options pricing model is a pricing model in a real-life environment and needs to take into account uncertainties in the environment such as randomness and fuzziness. As such, this paper makes use of fuzzy theory to construct an options pricing model based on an infinite pure jump Levy process in a fuzzy environment, with the drift, diffusion, and jump as trapezoidal fuzzy random variables. Following this, the Monte Carlo simulation algorithm is used to conduct numerical simulations, where the instrumental variable method is employed to increase the convergence rate of the simulation. A simulation experiment is also used to compare the pricing result of the Black–Scholes (BS) model, the variance gamma (VG) options pricing model in a certain environment, and the VG options pricing model in a fuzzy environment. The result of the analysis indicates that the VG options pricing model in a fuzzy environment is a feasible one, with the fuzzy interval narrowing as the option exercise price increases. On the other hand, the fuzzy interval widens as the option expiration date increases. Owing to the introduction of more uncertainties, the option price obtained under this model is higher than those of other models. The option price under the model is also more sensitive to changes in the jump parameter: as the jump parameter increases, the fuzzy interval narrows. Finally, an empirical examination using Tencent Holdings (HK.0700) and its stock options indicates that the expectation using fuzzy pricing is closer to the market price than that of the BS model. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley &amp
Sons, Inc.

This paper focuses mainly on issues related to the pricing of American options under a fuzzy environment by taking into account the clustering of the underlying asset price volatility, leverage effect and stochastic jumps. By treating the volatility as a parabolic fuzzy number, we constructed a Levy-GJR-GARCH model based on an infinite pure jump process and combined the model with fuzzy simulation technology to perform numerical simulations based on the least squares Monte Carlo approach and the fuzzy binomial tree method. An empirical study was performed using American put option data from the Standard &amp
Poor's 100 index. The findings are as follows: under a fuzzy environment, the result of the option valuation is more precise than the result under a clear environment, pricing simulations of short-term options have higher precision than those of medium- and long-term options, the least squares Monte Carlo approach yields more accurate valuation than the fuzzy binomial tree method, and the simulation effects of different Levy processes indicate that the NIG and CGMY models are superior to the VG model. Moreover, the option price increases as the time to expiration of options is extended and the exercise price increases, the membership function curve is asymmetric with an inclined left tendency, and the fuzzy interval narrows as the level set α and the exponent of membership function n increase. In addition, the results demonstrate that the quasi-random number and Brownian Bridge approaches can improve the convergence speed of the least squares Monte Carlo approach.

Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yen–dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules.

This paper develops a hybrid approach integrating an adaptive artificial neural network (ANN) and a fuzzy logic system for tackling class-imbalance problems. In particular, a supervised learning ANN based on Adaptive Resonance Theory (ART) is combined with a Tagaki-Sugeno-Kang-based fuzzy inference mechanism to learn and detect defects of a real large highly imbalanced dataset collected from a semiconductor company. A benchmark study is also conducted to compare the classification performance of the proposed method with other published methods in the literature. The real dataset collected from the semiconductor company intrinsically demonstrates class overlap and data shift in a highly imbalanced data environment. The generalization ability of the proposed method in detecting semiconductor defects is evaluated and compared with other existing methods, and the results are analyzed using statistical methods. The outcomes from the empirical studies positively indicate high potentials of the proposed approach in classifying the highly imbalanced dataset posing overlap class and data shift. (C) 2017 Elsevier Inc. All rights reserved.

Nowadays, traffic accidents is universally decreasing due to many advanced safety vehicle systems. To prevent the occurrence of a traffic accident, the first function that a safety vehicle system should accomplish is the detection of the objects in traffic situation. This paper presents a popular method called boosted HOG features to detect the pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we use boosted HOG features to get an satisfying result. In detecting pedestrians part, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate result. In detecting vehicles part, we use the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is the important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and could be segmented by setting a threshold.

Panoramic image mosaic is a technology to match a series of images which are overlapped with each other. Panoramic image mosaics can be used for different applications. Image mosaic has important values in various applications such as computer vision, remote sensing image processing, medical image analysis and computer graphics. Image mosaics also can be used in moving object detection with a dynamic camera. After getting the panoramic background of the video for detection, we can compare every frame in the video with the panoramic background, and finally detect the moving object. To build the image mosaic, SURF (Speeded Up Robust Feature) algorithm is used in feature detection and OpenCV is used in the programming. Because of special optimization in image fusion, the result becomes stable and smooth.

This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian-Cauchy PSO Neural Network converges faster and is immune to local minima.

Option pricing models are at core of financial area, and it includes various uncertain factors, such as the randomness and fuzziness. This paper constructs an jump Levy process by combining option pricing models with fuzzy theory, and it sets the drift, diffusion and trend terms as fuzzy random variable. Then, we adopts a Monte Carlo algorithm for numerical simulation, compares and analyses the variance gamma (VG) option pricing model through a simulation experiment, and determines the VG option pricing model and BS model pricing results. The results indicate that VG option pricing with fuzzy settings is feasible.

Usually it is hard to classify the situation where uncertainty of randomness and fuzziness exists simultaneously. This paper presents a rough set approach applying fuzzy random variable and statistical t-test to text-mine a large data repository of experts’ diagnoses provided by a Japanese power company. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the timeseries data as decision attributes. We also use merging rules to further analyze the time series data.

Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can reason for an unknown object using the decision rule. However, there are cases where too many decision rules are found. It is difficult to acquire knowledge from too many rules. In this paper, we propose a method to reduce the number of rules by merging decision rules. It is difficult to acquire knowledge from many rules. It is also difficult to find knowledge from rules with a large number of condition attributes. We propose a method to reduce condition attributes. We think that reducing the number of rules by reducing the condition attribute. We analyze time series data using this proposed method. Acquire knowledge for prediction from time series data using decision rule. TOPIX is used as time series data. Analyze TOPIX data and acquire knowledge for prediction.

Fuzzy Time series (FTS) has been widely applied to handle non-linear problems, such as enrollment estimation, weather prediction and stock index forecasting. FTS predicted values on the basis of an equal interval, which is determined the early stages of forecasting in the model. In this paper, we employed Genetic Algorithms (GA) to optimize the interval at first. Based on this, then Rough Set (RS) method is employed to recalculate the values. So the main purpose of this paper is to forecast a stock closing price by using the trend of the stock analyzed. We could identify trends of similar patterns from stock data to predict development of new data in the future. This proposed method is more efficient than the conventional FTS method.

Missing values are a common occurrence in a number of real world databases, and various statistical methods have been developed to address this problem, which is referred to as missing data imputation. In the detection and prediction of incipient faults in power transformers using Dissolved Gas Analysis (DGA), the problem of missing values is influential and has resulted in inconclusive decision-making. Previous methods used for handling missing data (e.g., Deleting cases with incomplete information or substituting the missing values with estimated mean scores), although simple to implement, are problematic because those methods may result in biased data models. Fortunately, recent advances in theoretical and computational statistics have led to more feasible techniques to address the missing data problem.

In this study, we consider a portfolio-optimization incorporated budget investment problem under managers' risk tolerance ambiguity. In order to capture the decision dynamics driven by the risk tolerance ambiguity, a two-stage adaptive optimization model is developed. The budget allocation is the first-stage decision, which is made before knowing each manager's actual risk tolerance level, and the portfolio selection conducted by each manager is the second-stage decision, which adapts to the manager's risk tolerance. We introduce the concept of risk-neutral budget threshold (RNBT) that is modeled by a fuzzy set granule, and upon which the ambiguous risk tolerance curve is constructed, which can realistically capture the managers' risk-averse and/or risk-seeking attitudes. Due to the (realistic) nonconvex/nonconcave structure of the risk tolerance curve, and the existence of the ambiguity, the resulting problem is essentially a nonconvex adaptive optimization problem under uncertainty. To achieve a robust modeling and an efficient solution, we first restructure and robustize the information of fuzzy RNBTs and then transform the developed model into a mixed integer linear programming (MILP), which can be handled efficiently by off-the-shelf mixed integer program solvers. Leveraging the derived MILP structure, we can use the Benders decomposition to further enhance the scalability of the model. Furthermore, some model extensions on robustizing the probability estimations are discussed. Finally, computational studies are performed to demonstrate the effectiveness and insights of the model.

In this study, we discuss multi-period portfolio selection problems when security returns are described as fuzzy random variables. The main concern of this work is to apply dynamic risk tolerance and expected return levels in mathematical modeling; i.e., these two indices of each period are influenced by the investment result of the previous period as well as human risk attitudes instead of static values over the entire investment horizon. Essentially, this assumption is based on the reality that investors tend to update targets when their wealth changes. In addition, fuzzy random variables are employed here to incorporate historical data with expert knowledge when estimating security future returns. Based on the above considerations, two multi-period portfolio selection models are built in light of the different risk attitudes. We then provide property analysis on complicated nonlinear optimization problems and derive several equivalents of the models, which can be solved by the existing dynamic programming. In general situations, a fuzzy random simulation-based particle swarm optimization algorithm is developed to search for approximate optima. The performance of this research is exemplified by a real market data-based case study in which the superiority of the dynamic strategy is demonstrated by a comparison with conventional approaches. (C) 2016 Elsevier Inc. All rights reserved.

In this research, quantum particle swarm optimization (QPSO) is utilized to solve multiobjective combined economic emission dispatch (CEED) problem formulated using cubic criterion function considering a uni wise max/max price penalty factor. QPSO is implemented on a 6-unit power generation system and compared with Lagrangian relaxation, particle swarm optimization (PSO) and simulated annealing (SA). The obtained results verified the effectiveness and demonstrate the robustness of QPSO method. This research suggests that QPSO can be used as an effective and robust tool in other power dispatch problems.

Economy, reliability and environmental friendly are primary goals when modeling modern unit commitment problems. In this study, we establish a multi-objective unit commitment model considering the above objectives. In particular, the pricing support for ultra-low emissions is addressed together with startup/shutdown, generation and environment concerns when calculating the operation cost of thermal units, which conforms the present situation of power markets, especially in China. To solve the complicated nonlinear model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed on a modified 26-thermal-unit test system, which demonstrates the superiority of this research.

Type-2 (T2) fuzzy set was introduced to model vagueness associated with primary membership function of type-1 (T1) fuzzy set. While it was invented to handle more fuzzy information, there are only a few algorithms (models) to deal with data in the form of T2 fuzzy variables given their three-dimensional features. To solve the problem, we define the expected value of a T2 fuzzy variable using credibility theory in this paper. And by substituting the expected value for the original T2 fuzzy set, the vertical uncertainties of data are transferred to horizontal ones without much distortion of information. Calculations between three-dimensional T2 fuzzy sets are thus transferred to two-dimensional range calculations between T1 fuzzy sets. Based on that principle, we also build a T2 fuzzy expected regression model and apply it to the arbitrage pricing theory. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This chapter examines hotel yield management from a game perspective using a duopoly situation of two hotels. The hotel yield management determines strategies by considering the number of available rooms in the Bertrand situation. Each hotel does not know the strategies adopted by a competitive hotel. We derive the strategy that realizes a maximum profit under a given situation and constraints. Furthermore, we validate the game-based strategy developed for hotel yield management. In the real world, a business manager adopts an optimum strategy of yield management to gain profits in the current conditions
after the initial strategy is chosen, however, managers continuously weigh new strategies and investments. Therefore, we import the method of real option. Such maneuvers and investments are required to build new strategies amidst competition in the industry.

Recent years have witnessed the ever increasing renewable penetration in power generation systems, which entails modern unit commitment problems with modelling and computation burdens. This study aims to simulate the impacts of manifold uncertainties on system operation with emission concerns. First, probability theory and fuzzy set theory are applied to jointly represent the uncertainties such as wind generation, load fluctuation and unit outage that interleaved in unit commitment problems. Second, a Value-at-Risk-based multi-objective approach is developed as a bridge of existing stochastic and robust unit commitment optimizations, which not only captures the inherent conflict between operation cost and supply reliability, but also provides easy-to-adjust robustness against worst-case scenarios. Third, a multi-objective algorithm that integrates fuzzy simulation and particle swarm optimization is developed to achieve approximate Pareto-optimal solutions. The research effectiveness is exemplified by two case studies: The comparison between test systems with and without generation uncertainty demonstrates that this study is practicable and can suggest operational insights of generation mix systems. The sensitivity analysis on Value-at-Risk proves that our method can achieve adequate tradeoff between performance optimality and robustness, thus help system operators in making informed decisions. Finally, the model and algorithm comparisons also justify the superiority of this research. (C) 2016 Elsevier Ltd. All rights reserved.

Camera tracking systems have become a common requirement in today's society. The availability of high-quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. In general, it is not easy to track human behavior in an environment with a large view. This paper aims to address four problems associated with large view in camera tracking system: 1) multiple targets in nonlinear motion; 2) relative size of the targeted object; 3) occlusion; and 4) processing time. This paper presents a new method of tracking human movements using global best local neighborhood oriented particle swarm optimization and model-based particle filter to address the above problems. The proposed method has been tested with an experimental module using several sets of video data provided by the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance and two other video streams of University of British Columbia (UBC) hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared with other reported works in the scientific literature.

Unit commitment, as one of the most important control processes in power systems, has been studied extensively in the past decades. Usually, the goal of unit commitment is to reduce as much production cost as possible while guaranteeing the power supply operated with a high reliability. However, system operators encounter increasing difficulties to achieve an optimal scheduling due to the challenges in coping with uncertainties that exist in both supply and demand sides. This study develops a day-ahead two-stage multi-objective unit commitment model which optimizes both the supply reliability and the total cost with environmental concerns of thermal generation systems. To tackle the manifold uncertainties of unit commitment in a more comprehensive and realistic manner, stochastic and fuzzy set theories are utilized simultaneously, and a unified reliability measurement is then introduced to evaluate the system reliability under the uncertainties of both sudden unit outage and unforeseen load fluctuation. In addition, a cumulative probabilistic method is proposed to address the spinning reserve optimization during the scheduling. To solve this complicated model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed to demonstrate the effectiveness of this research; we also justify its feasibility on test systems with generation uncertainty.

Methods of qualitative analysis, such as qualitative classification, have gained importance as an essential complement of existing quantitative analysis in numerous fields. Only a few models have been developed to deal with qualitative inputs in the form of type-2 fuzzy(T2F) sets properly, given that traditional defuzzification method like the Karnik-Mendel algorithm performs dimensionality reduction at the cost of loss of information. To improve the situation, we define the expected value and variance of T2F set in this paper. By using a combination of them, we transfer the vertical three-dimensional uncertainty of T2F set to horizontal range uncertainty without much distortion of information. Additionally, current classification models are unsuitable to the partial classification problem if an output is not fully assigned to a single class. We build a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM) combined with type-2 fuzzy expected regression (FER) to solve the partial classification problem as mentioned. This classifier (i.e. FER-FSVM) makes it possible to achieve the discrimination of output while characterizing membership for each class in terms of multidimensional qualitative inputs (attributes) in the form of T2F sets. FER-FSVM also can self-learn the data structure and shift between FER or FSVM for classification automatically, thus largely improving the efficiency of the classification process. The new model is almost 7 times more efficient than FSVM, as shown by our empirical experiments. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

In this study, we establish a bilevel electricity trading model where fuzzy set theory is applied to address future load uncertainty, system reliability as well as human imprecise knowledge. From the literature, there have been some studies focused on this bilevel problem while few of them consider future load uncertainty and unit commitment optimization which handles the collaboration of generation units. Then, our study makes the following contributions: First, the future load uncertainty is characterized by fuzzy set theory, as the various factors that affect the load forecasting are often assessed with some non-statistical uncertainties. Second, the generation costs are obtained by solving complicated unit commitment problems, rather than approximate calculations used in existing studies. Third, this model copes with the optimizations of both the generation companies and the market operator, where the unexpected load risk is particularly analyzed by using fuzzy value-at-risk as a quantitative risk measurement. Forth, a mechanism to encourage the convergence of the bilevel model is proposed based on fuzzy maxmin approach, and a bilevel particle swarm optimization algorithm is developed to solve the problem in a proper runtime. To illustrate the effectiveness of this research, we provide a test system-based numerical example and discuss about the experimental results according to the principle of social welfare maximization. Finally, we also compare the model and algorithm with conventional methods.

Financial markets are connected well these days. One class assets price performance is usually affected by movements of other classes of assets. However, the relationship between them is hard to trace and predict along with increase in complexity of markets behaviors these days. Nothing like stock market, money or bond market is an over-the-counter market, where assets prices are often presented in the form of classes of discrete quotations by traders subjective judgments, thus are hard to model and analyze. Given concern to this, we define the Type 2 fuzzy random variable (T2 fuzzy random variable) to quantify those bid/offer behaviors in this paper. Moreover, we build a T2 fuzzy random support vector regression (T2-FSVR)scheme to study relationships between these markets, thus form an effective trading strategy to predict the trend of market prices. We use matlab platform to implement and test the effectiveness of the new model, then train and test it with 2014 whole years price data of bond and money markets. We also compare T2-FSVRs prediction accuracy with type-2 fuzzy expected regression(T2-FER) and confidence-interval-based fuzzy random regression model(CI-FRRM). The result shows that T2-FSVR outperforms and has 98% accuracy while CI-FRRM has 81% accuracy and T2-FER has only 70% accuracy. Moreover,T2-FSVR can be developed into a automated trading strategy for practical business use, which is able to learn behaviors of different markets based on mass of available historical and real time data and earn profit automatically.

The parameter value determination is important to avoid the developed mathematical model is troublesome and may yield inappropriate results. However, estimating the weights of the parameter or objective functions in the mathematical model is sometimes not easy in real situations, especially when the values are unavailable or difficult to decide. Additionally, various uncertainties include in the statistical data makes common mathematical analysis is not competent to deal with. Hence, this paper presents the Fuzzy Random Regression approach to determine the coefficient whereby statistical data used contain uncertainties namely, fuzziness and randomness. The proposed methods are able to provide coefficient information in the model setting and consideration of uncertainties in the evaluation process. The assessment of coefficient value is given by Weight Absolute Percentage Error of Fuzzy Decision. It clarifies the results between fuzzy decision and non-fuzzy decision that shows the distance of different between both approaches. Finally, a real-life application of production planning models is provided to illustrate the applicability of the proposed algorithms to a practical case study.

Rough set theory was proposed by Z. Pawlak in 1982. This theory has high capability to mine knowledge based on decision rules from a database, a web base, a set and so on. The decision rule is widely used for data analysis as well. In this paper the decision rule is employed to reason for an unknown object. That is, the rough set theory is applied to analysis of economic time series data. An example shown in the paper indicates how to acquire knowledge from time series data. At the end we suggest its application to predictions.

This paper proposes Gaussian-PSO-based structural learning and fuzzy reasoning to optimize the weights and the structure of the Feed Forward Neural Network. The Neural Network is widely used for various applications; though it still has disadvantages such as learning capability and slow convergence. Back Propagation, the most used learning algorithm, has several difficulties such as the necessity for a priori specification of the network structure and sensibility to parameter settings. Recently, research studies have introduced evolutionary algorithms into the learning to improve its performance. The PSO is a population-based algorithm that has the advantage of faster convergence. However, the total number of the weights in the Neural Network determines the size of each particle, therefore the size of the network structure is computationally time consuming. The proposed method improves the learning and removes the stress by eliminating the necessity of determining a detailed network. (C) 2015 Elsevier B.V. All rights reserved.

This paper presents a memetic fuzzy ARTMAP (mFAM) model constructed using a grammatical evolution approach. mFAM performs adaptation through a global search with particle swarm optimization (PSO) as well as a local search with the FAM training algorithm. The search and adaptation processes of mFAM are governed by a set of grammatical rules. In the memetic framework, mFAM is constructed and it evolves with a combination of PSO and FAM learning in an arbitrary sequence. A benchmark study is carried out to evaluate and compare the classification performance between mFAM and other state-of-art methods. The results show the effectiveness of mFAM in providing more accurate prediction outcomes.

In this study, we formulate a double layer neural network based hybrid method to solve the quadratic bi-level programming problem. Our proposed algorithm comprises an improved artificial bee colony algorithm, a Hopfield network, and a Boltzmann machine in order to effectively and efficiently solve such problems. The improved artificial bee colony algorithm is developed for dealing with the upper level problem. The experiment results indicate that compared with other methods, the proposed double layer neural network based hybrid method is capable of achieving better optimal solutions for the quadratic bi-level programming problem.

Human tracking is one of the most important researches in computer vision. It is quite useful for many applications, such as surveillance systems and smart vehicle systems. It is also an important basic step for content analysis for behavior recognition and target detection. Due to the variations in human positions, complicated backgrounds and environmental conditions, human tracking remains challenging work. In particular, difficulties caused by environment and background such as occlusion and noises should be solved. Also, real-time human tracking now seems a critical step in intelligent video surveillance systems because of its huge computational workload. In this paper we propose a Particle Swarm Optimization based Support Vector Machine (PSO- SVM) to overcome these problems. First, we finish the preliminary human tracking step in several frames based on some filters such as particle filter and kalman filter. Second, for each newly come frame need to be processed, we use the proposed PSO-SVM to process the previous frames as a regression frame work, based on this regression frame work, an estimated location of the target will be calculated out. Third, we process the newly come frame based on the particle filter and calculate out the target location as the basic target location. Finally, based on comparison analysis between basic target location and estimated target location, we can get the tracked target location. Experiment results on several videos will show the effectiveness and robustness of the proposed method.

The radial basis function neural network trained with a dynamic decay adjustment (known as RBFNDDA) algorithm exhibits a greedy insertion behavior as a result of recruiting many hidden nodes for encoding information during its training process. In this chapter, a new variant RBFNDDA is proposed to rectify such deficiency. Specifically, the hidden nodes of RBFNDDA are re-organized through the supervised Fuzzy ARTMAP (FAM) classifier, and the parameters of these nodes are adapted using the Harmonic Means (HM) algorithm. The performance of the proposed model is evaluated empirically using three benchmark data sets. The results indicate that the proposed model is able to produce a compact network structure and, at the same time, to provide high classification performances.

This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate results. In the part of detecting vehicle, we select the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is an important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and can be segmented by setting a threshold. Finally, experimental results show that the detection of pedestrians and vehicles using boosted hog feature and linear svm combines the advantages of hog feature and adaboost classifier, and can achieve better detection results than the detector using conditional HOG features. At the end, the paper shows its efficiency and effectiveness using to application in real situations.

A fuzzy regression model illustrates the possibilities of the target system by including all data in the model. Inuiguchi et al., Tajima and Yabuuchi et al. are independently working on coinciding between the center of a possibility distribution and the center of a fuzzy regression model. Typically, samples influence and distort the shape of the interval model such as a fuzzy regression model if they are far from the center of data. Yabuuchi and Watada have developed a model for describing the system possibility using the center of a fuzzy regression model and an approach that mends the distortion of the model. In this paper, the approach to describe the system possibility and to remove influences of unusual data will be improved. And this approach will be compared with our previous approach using a numerical example.

In past days, although we have focused on to collect required data, we can get required information since many data are storage and disclosed. Therefore, it has become a new task to search efficiently required information.
Nowadays, the search engine such as Google, Bing and Baidu help us to search information in the internet. However, enormous number of search results is listed. In some cases, the number of search results can be more than a hundred million that are ranked by their relevancies to the search key words. It is difficult to find out the desired information because user's time and effort are required. In order to efficiently attain user's required information reach, although it is an effective way to rank data by their relevancies to the search key words, sometime it is better the way to summarize information. In this work, we propose summarizing approach for efficient search by k-medoids method. Without defining categories in advance, k-medoids method generates clusters with less susceptibility to noise. Experimental results verify our method's feasibility and effectiveness.

Eye tracking and brainwave are becoming parts of human machine interface. In automobiles are being integrated with advanced driver assistance systems to improve the safety and assists. To reduce car accidents, vehicles should be detected by other drivers or pedestrian in any situations. Nowadays, many countries use the function of daytime running light (DRL) as a safety measure to reduce car accidents by increasing the contrast between vehicles and the background. Third brake light (TBL) also significantly increases and being legal requirement on all vehicles around the world. Light emitting diodes (LED) and their bright lights offer unique design possibilities in lighting technology. This paper surveys the effectiveness of LED in eye recognition based on for vehicles.

Sensitivity Analysis is a method to evaluate the influence of each variable change. In portfolio selection model, it is essential to evaluate the sensitivity of each stock or security return rate in investment decision making. Investors look for selecting stable stocks or securities. For this purpose, sensitivity analysis should play a pivotal role. It is important for the decision-making to get both sensitivity and stability of each selection. This paper proposes a new portfolio-selection model (PSM) called the sensitivity-based portfolio selection models (SPSM). The SPSM model will focus on the sensitivity of the selected portfolio. In order to analyze the sensitivity of portfolio selection models, a sensitivity analysis will be introduced for calculating out insensitive stocks or securities with maximum return and minimum risk. Abstract environment.

Panoramic image mosaic is a technology to match a series of images which are overlapped with each other. Panoramic image mosaics can be used for different applications. Image mosaic has important values in various applications such as photogrammetry, computer vision, remote sensing image processing, medical image analysis and computer graphics. Image mosaics also can be used in moving object detection with a dynamic camera. After getting the panoramic background of the video for detection, we can compare every frame in the video with the panoramic background, and finally detect the moving object. To build the image mosaic, SURF (Speeded Up Robust Feature) algorithm is used in feature detection and OpenCV is used in the programming.

Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.

Recent research works show an increasing trend in analyzing agriculture problem because of its good applicability and usability. Also, the rapid development of mathematical model mitigates the difficulty to solve such abstract and uncertain problem. In this paper, the questions are discussed about a wine system, which contains some grape blocks and winery together. In the paper, a bilevel model is developed for obtaining the balance between profits and production quality.

In the field of services marketing, consumers can be segmented into separate subgroups with homogeneous service requirements in the process of consumer market segmentation. In this process, a computable number of consumers and the services that they can select are not difficult to handle. However, when there is a large consumer base and the services that the consumers can select have multiple shared and distinct characteristics, measurement and segmentation in polynomial time are extremely difficult. Therefore, in this paper, we propose a biologically systematic computational method that would be appropriate for the segmentation of consumer and service characteristics. We also demonstrate the application of this biologically systematic computational method to a services marketing problem model.

Information given in linguistic terms around real life sometimes is vague in meaning, as type-1 fuzzy set was introduced to modulate this uncertainty. Meanwhile, same word may result in various meaning to people, indicating the uncertainty also exist when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set attempt to express the hybrid uncertainty of both primary and secondary fuzziness, in order to address regression problems, we built a type-2 Linguistic Random Regression Model based on credibility theory. Confidence intervals are constructed for fuzzy input and output, and the proposed regression model give a rise to a nonlinear programming problem focus on a well-trained model, which would be helpful and useful in linguistic assessment cases. Finally, a numerical example is provided.

In real-world applications, sometimes randomness and fuzziness may coexist. In facility-location problems, the data expressed in natural language may contain vague information. We discuss the uncertainty included in demands in facility-location problems. The uncertain demand is called fuzzy demand in this paper. In the facility-location model, the parameters of fuzzy demand are determined by calculating the estimated expected value of the fuzzy demand, which is obtained by using the estimated parameters of the underlying probability distribution function of the fuzzy data. Moreover, we propose a defuzzification formula of the fuzzy demand called the realization of fuzzy demand. The defuzzification formula of fuzzy demand comprises the upper bound and the lower bound of the fuzzy demand. Moreover, the error of the fuzzy demand is assessed as the mean absolute percentage error of the fuzzy demand. Empirical studies show that we can solve real-life location problems by using the defuzzification formula of fuzzy demand and get higher profit in our facility-location model than by using conventional methods. (C) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper focuses on the impacts of exit strategy in existing fuzzy portfolio selection models. In the securities market, the term 'exit' means that the investor may sell his/her equity on account of some exogenous or endogenous incentives, especially when the security price becomes higher than his/her expectation or lower than his/her tolerance. It is a common problem that all investors need to face in the investment horizon. There have been various studies reported in the current literature employing fuzzy set theory to handle the uncertainty of portfolio selection. Nevertheless, none of these studies considers the influence caused by exit strategy which will be executed once a security price fluctuates out of the expected interval. In this paper, we first secure the future returns of each security by profit/loss exit points (prices) before rebuilding fuzzy portfolio selection models. Next, the properties of exit points are analyzed. Then a meta-heuristic method is proposed to analyze the differences between the new models' experimental results and those of previous methods. Finally, we discuss how to assign proper exit point values to different securities based on different risk attitudes, and apply our approach to a real application on the New York Stock Exchange. (C) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Multi-attribute decision-making is usually concerned with weighting alternatives, thereby requiring weight information for decision attributes from a decision maker. However, the assignment of an attribute's weight is sometimes difficult, and may vary from one decision maker to another. Additionally, imprecision and vagueness may affect each judgment in the decision-making process. That is, in a real application, various statistical data may be imprecise or linguistically as well as numerically vague. Given this coexistence of random and fuzzy information, the data cannot be adequately treated by simply using the formalism of random variables. To address this problem, fuzzy random variables are introduced as an integral component of regression models. Thus, in this paper, we proposed a fuzzy random multi-attribute evaluation model with confidence intervals using expectations and variances of fuzzy random variables. The proposed model is applied to oil palm fruit grading, as the quality inspection process for fruits requires a method to ensure product quality. We include simulation results and highlight the advantage of the proposed method in handling the existence of fuzzy random information.

The uncertainty in real-world decision making originates from several sources, i.e., fuzziness, randomness, ambiguity. These uncertainties exist in the problem description and in the preference information in the mathematical programming model. Handling such uncertainties in the decision making model increases the complexities of the problem and make the solution of the problem is difficult to solve. In this paper, a linear fractional programming is used to solve multi-objective fuzzy random based possibilistic programming problems to address the vague decision maker's preference (aspiration) and ambiguous data (coefficient), in a fuzzy random environment. The developed model plays a vital role in the construction of fuzzy multi-objective linear programming model, which is exposed to various types of uncertainties that should be treated properly. An illustrative example explains the developed model and highlights its effectiveness.

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We search for the law of similarity from time-series data using the rough sets.

Methods of qualitative analysis such as qualitative classification have gained importance as an essential complement of existing quantitative analysis in numerous fields, such as behavior finance, econometrics, and business management. Only a few models have been developed to deal with qualitative inputs (attributes), which appear in the form of T2F data. Additionally, classification models are unsuitable if an output point is not fully assigned to a single class. In this paper, we formulate a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM in brief) combined with Type-2 fuzzy expected regression (FER in brief) to deal with T2F inputs. This classifier(FER-FSVM in brief) makes it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, FER-FSVM can self-learn the data structure and shifted between FER or FSVM for classification automatically. It will largely shorten the computing time especially for large datasets by using linear structure of FER classifier to limit the size of non-linear classification region.

Reliability is an issue that has recently captivated the attention of researchers. Its goal is to develop new techniques to design more reliable systems, which can operate without failing during operation. A result of this growth in technology is an increase in the complexity and susceptibility of more complex systems. The principal objective of redundancy allocation is to maximize the availability of a system while reducing the cost, volume or weight. This paper proposes an Estimation-of-Distribution Algorithm (EDA) approach as a new meta-heuristic method to solve a redundancy allocation problem (RAP) for a high security control system.

This paper proposes a Gaussian-PSO algorithm which provides the optimized parameters for Feed Forward Neural Network. Recently the Feed Forward Neural Network is widely used in various applications as a result of its advantages such as learning capability, auto-organization and auto-adaptation. However the Neural Network has the disadvantage itself to slowly converge and get easily trapped in a local minima. In this paper, Gaussian distributed random variables are used in the PSO algorithm to enhance its performance and train the weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian PSO-Neural Network faster converges and is immuned to the local minima.

In this paper, we studied a multi-objective portfolio selection problem with piecewise linear transaction costs in a fuzzy environment. Transaction costs are expenses incurred when buying or selling securities and they are a burden on investors who frequently make trades to balance their portfolio. To better evaluate portfolio performance with transaction costs, this paper extends a previous study to a new model called fuzzy multi-objective portfolio selection model with piecewise linear transaction costs. A fuzzy simulation-based particle swarm optimization algorithm is designed to solve the model considering investors' individual risk attitude. In addition, several numerical examples are provided to illustrate the effectiveness of this model and algorithm. A conclusion is drawn at the end of this paper. © 2014 The Institute of Electrical Engineers of Japan.

Recently, fuzzy set theory has been widely employed in building portfolio selection models where uncertainty plays a role. In these models, future security returns are generally taken for fuzzy variables and mathematical models are then built to maximize the investment profit according to a given risk level or to minimize a risk level based on a fixed profit level. Based on existing works, this paper proposes a portfolio selection model based on fuzzy birandom variables. Two original contributions are provided by the study: First, the concept of technical analysis is combined with fuzzy set theory to use the security returns as fuzzy birandom variables. Second, the fuzzy birandom Value-at-Risk (VaR) is used to build our model, which is called the fuzzy birandom VaR-based portfolio selection model (FBVaR-PSM). The VaR can directly reflect the largest loss of a selected case at a given confidence level and it is more sensitive than other models and more acceptable for general investors than conventional risk measurements. To solve the FBVaR-PSM, in some special cases when the security returns are taken for trapezoidal, triangular or Gaussian fuzzy birandom variables, several crisp equivalent models of the FBVaR-PSM are derived, which can be handled by any linear programming solver. In general, the fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO) is designed to find the approximate optimal solution. To illustrate the proposed model and the behavior of the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide a discussion of some existing approaches. Copyright © 2014 The Institute of Electronics, Inf rmation and Communication Engineers.

Multiplex Fluorescent In Situ Hybridization (M-FISH) is a multi-channel chromosome image generating technique that allows colors of the human chromosomes to be distinguished. In this technique, all chromosomes are labelled with 5 fluors and a fluorescent DNA stain called DAPI (4 in, 6-Diamidino-2-phenylindole) that attaches to DNA and labels all chromosomes. Therefore, a M-FISH image consists of 6 images, and each image is the response of the chromosome to a particular fluor. In this paper, we propose a genetic interval type-2 fuzzy c-means (GIT2FCM) algorithm, which is developed and applied to the segmentation and classification of M-FISH images. Chromosome pixels from the DAPI channel are segmented by GIT2FCM into two clusters, and these chromosome pixels are used as a mask for the remaining five channels. Then, the GIT2FCM algorithm is applied to classify the chromosome pixels into 24 classes, which correspond to the 22 pairs of homologous chromosomes and two sexual chromosomes. The experiments performed using the M-FISH dataset show the advantages of the proposed algorithm.

After obtaining search results through web search engine, classifying into clusters enables us to quickly browse them. Currently, famous search engines like Google, Bing and Baidu always return a long list of web pages which can be more than a hundred million that are ranked by their relevancies to the search key words. Users are forced to examine the results to look for their required information. This consumes a lot of time when the results come into so huge a number that consisting various kinds. Traditional clustering techniques are inadequate for readable descriptions. In this research, we first build a local semantic thesaurus (L.S.T) to transform natural language into two dimensional numerical points. Second, we analyze and gather different attributes of the search results so as to cluster them through on density analysis based K-Medoids method. Without defining categories in advance, K-Medoids method generates clusters with less susceptibility to noise. Experimental results verify our method's feasibility and effectiveness.

The expectation function of fuzzy variable is an important and widely used criterion in fuzzy optimization, and sound properties on the expectation function may help in model analysis and solution algorithm design for the fuzzy optimization problems. The present paper deals with some analytical properties of credibilistic expectation functions of fuzzy variables that lie in three aspects. First, some continuity theorems on the continuity and semicontinuity conditions are proved for the expectation functions. Second, a differentiation formula of the expectation function is derived which tells that, under certain conditions, the derivative of the fuzzy expectation function with respect to the parameter equals the expectation of the derivative of the fuzzy function with respect to the parameter. Finally, a law of large numbers for fuzzy variable sequences is obtained leveraging on the Chebyshev Inequality of fuzzy variables. Some examples are provided to verify the results obtained.

In this paper, for solving imbalanced classification problem, more attention is placed on data points in the boundary area between two classes. The fuzzy k-nearest neighbors algorithm, which has good performance in conventional classification problems, is adapted here to solve imbalanced classification problems, where G-mean accuracy is used to evaluate our proposal method and compare it with other approaches.

Purpose - The purpose of this paper is to investigate optimal decision methods under a cooperative situation in two-echelon logistic models.
Design/methodology/approach - The authors propose the optimal strategies of exporters in the three types of rival game behaviors: Stackelberg, Collusion, and Cournot, each of which provides the optimal decision for the duopolistic shippers and the oligopolistic forwarders in each scenario.
Findings - From the empirical studies the paper finds that among three scenarios, the oligopolistic treatment of forwarders' actions demonstrates that Stackelberg behavior can carry out the maximum profit, and Collusion game can achieve the maximum profit for the shippers.
Originality/value - Proposed an optimal decision methods in two-echelon logistic models.

Processing an increasing volume of data, especially in industrial and manufacturing domains, calls for advanced tools of data analysis. Knowledge discovery is a process of analyzing data from different perspectives and summarizing the results into some useful and transparent findings. To address such challenges, a thorough extension and generalization of well-known techniques such as regression analysis becomes essential and highly advantageous. In this paper, we extend the concept of regression models so that they can handle hybrid data coming from various sources which quite often exhibit diverse levels of data quality. The major objective of this study is to develop a sound vehicle of a hybrid data analysis, which helps in reducing the computing time, especially in cases of real-time data processing. We propose an efficient real-time fuzzy switching regression analysis based on a genetic algorithm-based fuzzy C-means associated with a convex hull-based fuzzy regression approach. The method enables us to deal with situations when one has to deal with heterogeneous data which were derived from various database sources (distributed databases). In the proposed design, we emphasize a pivotal role of the convex hull approach, which is essential to alleviate the limitations of linear programming when being used in modeling of real-time systems. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution.
Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.

Load forecasting has become one of the core research topics in the power system. As power load has time-variant characteristics and nonlinear characteristics, different computational intelligent techniques, neural networks (NN) in particular, are used in short-term load forecasting (STLF) to make it more effective. This study proposes a Particle Swarm Optimization (PSO)-based neural network with support vector machine (NN-SVM) model to predict the power load in short-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and PSO. There are two stages in the proposed model. The first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast for short-term load forecasting (STLF). In the process of SVM training and NN learning, PSO is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF.

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We acquire knowledge from the time-series data including large variation. And we compare the data including large variation and normal data.

Nonparametric statistical tests are a distribution-free method without any assumption that data are drawn from a particular probability distribution. In this paper, to identify the distribution difference between two populations of fuzzy data, we derive a function that can describe continuous fuzzy data. In particular, the Kolmogorov-Smirnov two-sample test is used for distinguishing two populations of fuzzy data. Empirical studies illustrate that the Kolmogorov-Smirnov two-sample test enables us to judge whether two independent samples of continuous fuzzy data are derived from the same population. The results show that the proposed function is successful in distinguishing two populations of continuous fuzzy data and useful in various applications. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The objective of our research is to build a statistical test that can evaluate different risks of a portfolio selection model with fuzzy data. The central points and radiuses of fuzzy numbers are used to determine the portfolio selection model, and we statistically evaluate the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk evaluation of the portfolio selection model with interval values. We conclude that the fuzzy statistical test enables us to evaluate a stable expected return and low risk investment with different choices for k, which indicates the risk level. The results of numerical examples show that our method is suitable for short-term investments.

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We Knowledge Acquisition If the data has changed greatly.

A fuzzy autocorrelation model which we proposed is a fuzzy time-series model using a fuzzy autocorrelation coefficient. A fuzzy autocorrelation coefficient obtained by the fuzzy operation tends to increase its ambiguity. On the other hand, center values of fuzzy autocorrelation coefficients and predicted values by our model are coincided with AR model. Therefore, we can understand our model describes a possibility of a time-series system by the interval of our model. In addition this, our model is able to get describe the possibility with high accuracy. However, forecasted and predicted values of our model by using fuzzified stock prices were unnatural sometimes. In this paper, we will use fuzzy random variables to solve this problem. This is a fuzzy autocorrelation model with confidence intervals of fuzzy random data.

Chaos theory is widely employed to forecast near-term future values of a time series using data that appear irregular. The chaotic short-term forecasting method is based on Takens' embedding theorem, which enables us to reconstruct an attractor in a multi-dimensional space using data that appear random but rather are deterministic and geometric in nature. It is difficult to forecast future values of such data based on chaos theory if the information that the data provide cannot be reconstructed through wavelet transformation in a sufficiently low-dimensional space. This paper proposes a method to embed data in a small-dimensional space. This method enables us to abstract the chaotic portion from the focal data and increase forecasting precision. Chaotic methods are employed to forecast near-term future values of uncertain phenomena. The method makes it possible to restructure an attractor of given timeseries data set in a multidimensional space using Takens' embedding theory. However, many types of economic time-series data are not sufficiently chaotic. In other words, it is difficult to forecast the future trend of such economic data even based on chaos theory. In this paper, time-series data are divided into wave components using a wavelet transform. Some divided components of time-series data exhibit much more chaotic behavior in the sense of correlation dimension than the original time-series data. The highly chaotic nature of the divided components enables us to precisely forecast the value or the movement of the time-series data in the near future. The up-and-down movement of the TOPICS value is shown to be well predicted by this method, with 70% accuracy.

Rough set methods are often employed for reducting decision rules. The specific techniques involving rough sets can be carried out in a computational manner. However, they are quite demanding when it comes computing overhead. In particular, it becomes problematic to compute all minimal length decision rules while dealing with a large number of decision rules. This results in an NP-hard problem. To address this computational challenge, in this study, we propose a method of DNA rough-set computing composed of computational DNA molecular techniques used for decision rule reducts. This method can be effectively employed to alleviate the computational complexity of the problem. © Springer-Verlag Berlin Heidelberg 2013.

The objective of economic analysis is to interpret the past, present or future economic state by analyzing economic data. Economic analyses are typically based on the time-series data or the cross-section data. Time-series analysis plays a pivotal role in analyzing time-series data. Nevertheless, economic systems are complex ones because they involve human behaviors and are affected by many factors. When a system includes substantial uncertainty, such as those concerning human behaviors, it is advantageous to employ a fuzzy system approach to such analysis. In this paper, we compare two fuzzy time-series models, namely a fuzzy autoregressive model proposed by Ozawa et al. and a fuzzy autocorrelation model proposed by Yabuuchi andWatada. Both models are built based on the concepts of fuzzy systems. In an analysis of the Nikkei Stock Average, we compare the effectiveness of the two models. Finally, we analyze tick-by-tick data of stock dealing by applying fuzzy autocorrelation model.

Rough sets enable us to mine knowledge in the form of IF-THEN decision rules from a data repository, a database, a web base, and others. Decision rules are used to reason, estimate, evaluate, and forecast. The objective of this paper is to build the rough sets-based model for analysis of time series data with tick-wise price fluctuations where knowledge granules are mined from the data set of tickwise price fluctuations. We show how a method based on rough sets helps acquire the knowledge from time-series data. The method enables us to obtain IF-THEN type rules for forecasting stock prices.

In security market, the term exit means that the investor may sell his equity on account of some exogenous or endogenous incentives, especially when security price becomes higher than his expectation or lower than his tolerance. It is a common problem that all the deciders need to face in the investment horizon. However, there have been few studies probe into the influences caused by such strategy in fuzzy environment. Therefore, in this work, we use the exit strategy to secure security future returns and rebuild fuzzy portfolio selection models. Then, we discuss about the exit points and employ one meta-heuristic method to solve the proposed models. We also analyze the differences between the new models' experimental results and that of previous methods.

A database has a plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Classification analysis performs a very important rule in pattern recognition field as one core research topics. Algorithms like support vector machine (SVM) and artificial network (ANN) have been proposed to perform binary classification according to the distribution. But these traditional classification algorithms can hardly performs the satisfied result for imbalanced dataset. In this paper, we proposed to perform a model on the basis of Particle Swarm Optimization (PSO) and support vector machine (SVM) for a large imbalanced dataset. This model is named PSOSVC (Particle Swarm Optimization support vector classification) model. Recently, PSO is proposed used as a meta heuristic frame work for the large imbalanced classification. The SVM also shows high performance in balanced binary classification, so a novel model combined both support vector classification (SVC) and PSO is introduced to improve the classification accuracy. In this paper, G-mean is used to evaluate the final result. Performance in the final part of this paper the proposed method is compared with some conventional models, the results will show the high performance for imbalanced dataset classification by using the proposed method.

In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) to undertake data classification problems is proposed. The new network is formed by integrating the learning algorithm of the Fuzzy ARTMAP (FAM) neural network into RBFNDDA. The proposed RBFNDDA-FAM network inherits the salient features of FAM and overcomes the shortcomings of the original RBFNDDA network. The effectiveness of RBFNDDA-FAM is demonstrated using two benchmark problems. The first involves an artificial data set whereas the second uses a medical data set related to thyroid diagnosis. The results from these studies are compared, analyzed, and discussed. The outcomes positively reveal the potentials of RBFNDDA-FAM in learning information with a compact network architecture, in addition to high classification performances.

Fuzzy set theory has been applied to build various portfolio selection models in the past decades. Based on the knowledge of previous studies, this paper proposes a new portfolio selection model with technical pattern-based fuzzy birandom variables. There are two innovations in the work: The concept of technical pattern is combined with fuzzy set theory to use the fuzzy birandom variables as security returns; The fuzzy birandom Value-at-Risk (VaR) is introduced to build the mathematical model, named the fuzzy birandom VaR-based portfolio selection model (FBR-PSM). Then, fuzzy simulation is extended to the fuzzy birandom case to obtain a general solution to the FBR-PSM, which is called as fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO). To illustrate the performances of the FBR-PSM and the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide further discussions.

Information in real life may have linguistically vagueness. Thus, type-1 fuzzy set was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means uncertainty also exists when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, type-2 fuzzy variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of type-2 fuzzy regression model is built based on credibility theory, and is called the T2 fuzzy expected value regression model. The new model will be developed into two forms: form-A and form-B. This paper is a further work based on our former research of type-2 fuzzy qualitative regression model.

Conventionally, we use a chi-square test of homogeneity to determine whether the cell probabilities of a multinomial are equal. However, this process of testing hypotheses is based on the assumption of two-valued logic. If we collect questionnaire data using fuzzy logic, i.e., we record the category data with memberships instead of with a 0-1 type, then the conventional test of goodness-of-fit will not work. In this paper, we present a new method, the fuzzy chi-square test, which will enable us to analyze those fuzzy sample data. The new testing process will efficiently solve the problem for which the category data are not integers. Some related properties of the fuzzy multinomial distribution are also described.

The operations of a project team play an important role in the discussion of building high performance project teams. This study focuses on building a model for high performance project teams. First, success and failure factors are evaluated across aspects of a project team that affect team effectiveness. Second, effective teams are analyzed to clarify what defines a high performance project team. Third, this analysis is combined with past results to build a model for high performance project teams. The model for high, performance project teams is evaluated by fuzzy multi-criterion decision-making (fuzzy MCDM). The results show that all of the criteria have interactions, but that team effectiveness standard is the most influential dimension. On the contrary, the team process is the least influential dimension. From the viewpoint of experts, the most important ones of the 17 evaluation criteria are performance and satisfaction.

The enhancement of load forecasting has become one of the core research topics in the energy field. Because power load has both time-variant and nonlinear characteristics, different types of methods, neural networks (NN) in particular, have been applied to power load forecasting. This study proposes a real-valued genetic algorithm (RGA)-based neural network with support vector machine (NN-SVM) model to predict the power load in both short-term and mid-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and RGA. The model consists of two stages. In short-term load forecasting (STLF), the first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast. Similar operations are used in mid-term load forecasting (MTLF). In the process of SVM training and NN learning, RGA is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF. Also it is able to function well in MTLF.

In this study, we propose an improved fuzzy multi-objective portfolio selection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is used to measure the exact future risk in term of loss. Conversely, variance is utilized to make the selection more stable. In this case, the proposed VaR-MOPSM can provide investors with more significant information for decision-making. To solve this model, we designed a distance based particle swarm optimization algorithm. Finally, the proposed model and algorithm are exemplified by some numerical examples. The experimental results show that the model and algorithm are effective in solving the fuzzy VaR-MOPSM.

Traffic accidents cause tragic loss of life, property damage and substantial congestion to transportation systems. A large percentage of crashes occur at or near intersections. Therefore, traffic signals are often used to improve traffic safety and operations. The objective of this study is to present a significant and effective method of determining the optimal investment involved in retrofitting signals with light emitting diode (LED) units. In this study, the reliability and risks of each unit are evaluated using a variance-covariance matrix, and the effects and expenses of replacement are analyzed. The mean-variance analysis is formulated as a mathematical program with the objectives of minimizing the risk and maximizing the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve problems defined by mixed-integer quadratic programming, and this model is employed in the mean-variance analysis. Our method is applied to an LED signal retrofitting problem. This method enables us to select results more effectively and enhance decision-making.

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We inspect whether the knowledge varies according to the difference in stocks brand.

A possibilistic regression model illustrates the potential possibilities inherent in the target system by including all data in the model. Tanaka and Guo employ exponential possibility distribution to build a model, while Inuiguchi et al. and Tajima are independently working on coinciding between the center of a possibility distribution and the center of a possibilistic regression model. Typically, samples influence and distort the shape of the model if they are far from the center of data. Yabuuchi and Watada have developed a model for describing the system possibility using the center of a possibilistic fuzzy regression model and an approach that mends the distortion of the model. The objective of this paper is to analyze the Japanese economy using our model, and to show the usefulness of our model by analysis results.

This study is concerned with the development of marketing strategies for mineral water based on consumers' taste preferences, by analyzing the taste components of mineral water. In this study, we used a twodimensional analysis to classify taste data. We conducted a correlation analysis to identify the characteristics of taste data. We applied a combination of principal component analysis and self-organizing map to classify mineral water tastes. Based on this evaluation, we identified some marketing strategies in the conclusion. According to this study, the taste of mineral water is not determined by the origin and is not influenced by the hardness of the water.

Today, security and surveillance systems are required not only to track the motions of humans but also, in some situations, to recognize and measure biometric features such as width and length. Few methods have been proposed for biometric height measurement in human tracking. Some studies have shown that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model to measure the biometrics of human tracking. This tracking method can show humans' and objects' size in a picture so that, if we put this picture in a frame of axes, we can calculate the height and other biometric lengths. To obtain the most accurate results for biometric length surveillance, we need a tracking methodthat is more exact than conventional tracking results. Combining tracking and detection methods using a particle swarm optimization-particle filter shows results with great accuracy in human tracking.

This article presents a novel integrated method in which bio-soft computational and tabu search methods are both used to solve a multi-task project scheduling problem. In this scheduling problem, the main challenge is to determine the most reliable completion time. To solve this problem, we first use our proposed bio-soft computational method. Next, a tabu search method is used to verify the final results of the bio-soft computational method. The biosoft computational method includes molecular techniques, and the tabu search method is an intelligent optimization technique. Based upon this integrated method's success solving a multi-task project scheduling problem, this article proposes that this method can help decision makers in their computational project scheduling. © 2012 ACADEMY PUBLISHER.

The objective of our research is to build a statistical test that can evaluate the sensitivity of a portfolio selection model with fuzzy data. The central point and radius are used to determine the portfolio selection model and we make a decision for the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk of the portfolio selection model with interval values. We conclude that the evaluation by the fuzzy statistical test enables us to obtain a stable expected return and low risk investment with different choices based on the risk level k, which is taken for the risk level.

Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis.
In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. This confidence intervals has an essential role in dealing with fuzzy random data on our fuzzy autocorrelation model which we have presented. We analyze tick-by-tick data of stock dealing and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model which we propose in this paper.

In this paper, we formulate a qualitative classification model by means of qualitative fuzzy regression preset-based fuzzy support vector machine (FQR-FSVM). This new model will make it possible to achieve discrimination of output while characterizing membership for each class in terms of multi-dimensional qualitative inputs (attributes). Moreover, the new model will largely shorten the computing time especially for large database by using linear preset of fuzzy qualitative regression classifier to limit the non-linear classification region.

This research concerns with the development of marketing strategy of mineral water based on people's taste preference by analyzing taste components of mineral water. A two-dimensional analysis has been used in classifying tastes' data. The characteristics of data are recognized in tastes of mineral water by correlation analysis. A combination of Principal Component Analysis and Self-organizing Map is applied to classify the tastes of mineral water. Some marketing strategies are concluded after the evaluation.

This paper deals with optimal decision methods under a cooperative situation of the two-echelon model among logistic service providers (LSPs) of Stackelberg structure. Assuming duopolistic shippers and oligopolistic forwarders, the shippers perform as a leader and declare their service to both the forwarders after determining their price and quantity independently under shippers' scheme. The objective of this study is to obtain the optimal strategies of exporters in the three types of rival game behaviours: Stackelberg, Collusion and Cournot, each of which provides the optimal decision for the duopolistic shippers and the oligopolistic forwarders in each scenario. The result of a real situation indicates that: (i) among three scenarios, the oligopolistic treatment of forwarders' actions shows that Stackelberg behaviour can carry out the maximum profit, and (ii) Collusion game can achieve the maximum profit for the shippers.

The qualitative regression analysis models quantitatively change in the qualitative object variables by using qualitative values of multivariate data (membership degree or type I fuzzy set), which are given by subjective recognitions and judgments. From fuzzy set-theoretical points of view, uncertainty also exists when associated with the membership function of a type I fuzzy set. It will have much impact on the fuzziness of the qualitative objective external criterion. This paper is trying to model the qualitative change of external criterion's fuzziness by applying type II fuzzy set (we will use type II fuzzy set as well as type II fuzzy data in this paper). Here, qualitative values are assumed to be fuzzy degree of membership in qualitative categories and qualitative change in the objective external criterion is given as the fuzziness of the output.

The objective of this paper is to build a model for the linguist random regression model as a vehicle to solve linguistic assessment given by experts. The difficulty in the direct measurement of certain characteristics makes their estimation highly impressive and this situation results in the use of fuzzy sets. In this sense, the linguistic treatment of assessments becomes essential when fully reflecting the subjectivity of the judgment process. When we know the attributes assessment, the linguistic regression model get the total assessment.

Rough set and its method were proposed by Z. Pawlak in 1982. This method enabled up to mine knowledge granules as decision rules from a database, a web base, a set and so on. The decisions rule can be applicable for data analysis as well. And the decision rules to reason, estimate, evaluate, or forecast an unknown object. The objective of this paper is to apply the rough set theory time series data and to mine. Knowledge granules are minded from the data set of tick-wise price fluctuations.

The purpose of economic analysis is to interpret the history, present and future economic situation based on analyzing economical time series data. The autoregression model is widely used in economic analysis to predict an output of an index based on the previous outputs. However, in real-world economic analysis, given the co-existence of stochastic and fuzzy uncertainty, it is better to employ a fuzzy system approach to the analysis. To address regression problems with such hybridly uncertain data, fuzzy random data are introduced to build the autoregression model. In this paper, a fuzzy random autoregression model is introduced and to solve the problem, we resort to some heuristic solution based on sigma-confidence intervals. Finally, a numerical example of Shanghai Composite Index is provided.

The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.

A real-life application faces various kinds of inherent uncertainties which occurs simultaneously. To find solution, formulating real world problem into mathematical programming model is challenging. Uncertain parameters in a problem model can be characterized as vagueness, ambiguous and random of the information. Such uncertainties make the existing multi-objective model incapable of handling such situations. Thus, in this paper we present the multi-objective decision model from the perspective of possibilistic programming approach to scrutinize the uncertainties in the decision making. The proposed concept can be used to build model for multi-objective problem which is exposed with various types of uncertainties. We include an illustrative example to explain the model, and highlight its advantages.

The code of occupational health and safety (OHS) is an influential regulation to improve the on-the-job safety of employees. A number of factors influence the planning and implementation of OHS management systems (OHSMS). The evaluation of OHSMS practice is the most important component when forming a health and safety environmental policy for employees. The objective of this research is to develop an intelligent data analysis (IDA) in which possibilistic regression being endowed with a convex hull approach is used to support the analysis of essential factors that influence OHSMS. Given such subjective terms, the obtained samples can be conveniently regarded as fuzzy input/output data represented by membership functions. The study offers this vehicle of intelligent data analysis as an alternative to evaluate the influential factors in a successful implementation of OHS policies and in this way decrease an overall computational effort. The obtained results show that several related OHSMS influential factors need to be carefully considered to facilitate a successful implementation of the OHSMS procedure. (C) 2011 Elsevier Ltd. All rights reserved.

In IT corporations, it is essential to increase competitive advantages and organizational performance. Employees are critical to a company's success. A new research method is needed to quantify employees' influence on building relationships with customers and to facilitate human resource and customer relationship management. Rough sets theory is a mathematical approach to dealing with vagueness and uncertainty. It can change a qualitative problem into a quantitative one and produce a possible solution by providing useful and valuable information and guidelines for decision making. The objective of this study is to determine through the use of analysis analyzed with rough employee characteristics and behaviors that yield positive or negative relationships with customers. The rough set approach distinguishes between these two groups and leads us to suggest policies to improve human resource and customer relationship management and development. The proper management of employees and customers will ensure project success and good corporate performance. Quality is an attribute that is important for products as well as for management and the company itself. The development and promotion of personnel resources is indispensable for improving the quality of a company's management. Management quality is closely related to corporate culture and a sense of social responsibility. Therefore, personnel resource development and personnel training for employees should be emphasized. In the main discussion of this paper, information was gathered from engineers at a regional IT company through questionnaires and their observable talents were analyzed. The research addressed questions such as what kinds of values should be promoted. An attempt was made to clarify the relation between QWL (Quality of Working Life) and personnel training. This paper suggests that the management quality and CSR (Corporate Social Responsibility) of regional companies is closely related with the quality and improvement of their growth. © Springer-Verlag Berlin Heidelberg 2011.

DNA-based computation is one of the latest computation paradigms. Compared to conventional methods that obtain their end results via electronic processes, a DNA-based approach obtains its result from bio-chemical reactions. It is essential in this approach for all experimental processes to be performed without fault. However, some errors may occur while carrying out these bio-chemical experiments. Consequently, it is necessary to overcome their weaknesses. The aim of this study is to examine the robustness of DNA-based techniques in solving a clustering problem. In the broadest sense, robustness can be defined as being able to withstand stresses, pressures, or changes in procedure or circumstance. To examine the robustness of the approach, this research examined the impact of error or added noise on DNA-based procedure results. Comparative studies of different error sets are also provided here. Additionally, two well-known conventional clustering algorithms (Fuzzy C-means and k-means) were applied to the same error sets, to study the reliability and validity of results when comparing DNA-based clustering. © 2011, TSI® Press.

Since biocomputing plays an essential role in the biological and medical fields, the catalytic features of enzymes can be utilized in medical applications as biosensor. In this study, we tried to build an enzymatic system as biosensor equipment application to measure blood sugar with sequential reactions of enzymes, which express logic output signals with latent fluorophore as a reporter. The expected biosensor will analyze three molecules sucrose, maltose and ATP existing in human blood, by adding some blood into the system consisted of various enzymes. Sucrose and Maltose will be decomposed into glucose
moreover, glucose and ATP have critical influence of glycolysis which shows one crucial reaction of human metabolism. The output signal was visualized by high throughput fluorescence that originated from the long-wavelength latent fluorogenic substrate-Salicylate hydroxylase (SHL) reaction. The experiment results of this research have showed the possibility to determine the combinations of the three important components, by setting the concentration of each input molecules to specific threshold values. Further researches are required to find out the relationship between the specific concentration and measure glucose results, especially in the medical field. © 2011 Springer-Verlag.

Rough sets theory was proposed by Z. Pawlak in 1982. This theory enables us to mine knowledge granules through a decision rule from a database, a web base, a set and so on. We can apply the decision rule to reason, estimate, evaluate, or forecast unknown objects. In this paper, the rough set model is used to analyze of time series data of tick-wise price fluctuation, whereknowledge granules are mined from the data set of tick-wise price fluctuations.

The objective of this research is to realize structural learning within a Boltzmann machine (BM), which enables the effective solution of problems defined in terms of mixed integer quadratic programming. Simulation results show that computation time is up to one fifth faster than conventional BMs. The computational efficiency of the resulting double-layer BM is approximately expressed as the ratio n divided by N, where n denotes the number of selected units (neurons/nodes), and N the total number of units. The double-layer BM is applied to efficiently solve the mean-variance problem using mathematical programming with two objectives: the minimization of risk and the maximization of expected return. Finally, the effectiveness of our method is illustrated by way of a light emitting diodes (LED) signal retrofit example. The double-layer BM enables us to not only obtain a more effective selection of results, but also enhance effective decision making. The results also enable us to reduce the computational overhead, as well as to more easily understand the structure. In other words, decision makers are able to select the best solution given their respective points of view, by means of the alternative solution provided by the proposed method.

With recent trends in utilizing renewable power to develop sustainable energy sources, WTG and PV are increasingly viable economic alternatives for sustainable power generation from conventional fossil fuels. Therefore, multi-state models are being generated to solve the intermittent power production problem of wind turbine generator (WTG) and photo voltaic (PV). However, a disadvantage of these units is the generation of highly variable electricity at several different timescales from hourly, daily, and seasonally. Related to variability is the short-term (hourly or daily) predictability of wind plant output. These problems result in serious damage to sustainable service concerns in both the design and operation of WTG and PV systems. Large scale systems can provide a solution to overcome sustainable service problem but are costly. A sufficient design will cause outages that lead to certain cost losses on the customer side. Therefore, planning a reasonable size is a major dilemma.
For this objective, the three models of load model, generation model, and service cost model should be built. For the first two models, the loss of load expected (LOEE) and the loss of load expected (LOLE) can be calculated. Then, this reliability characteristic is evaluated with the existing system of generation units to decide the range of required renewable generators. Finally, the cost model is constructed with consideration of the sustainable service cost.
The fuel and operation costs obviously contribute a rather large portion to the utility cost. From the total service cost chart, the amount that the sustainable service worth is tremendous when forced outage at a high level occurs and the customer is not fulfilled. However, the utility cost becomes the primary of the total service costs when sufficient capacity is constructed in the system, which is invested mainly to the conventional generating. The utility cost of renewable energy changes with an increases in WTG and PV but remains steady. With regard to the excessive variable cost, fixed cost, and capital cost of conventional generating system, the investment cost and maintenance cost are relatively insignificant. If renewable energy replaces conventional generators, particularly thermal sources, this component of utility costs will not occur. Also, a certain service level is ensured to supply the load.

In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables.
To address regression problems in presence of such hybrid uncertain data, fuzzy random variables are introduced in this study, and serve as an integral component of regression models. A new class of fuzzy regression models based on fuzzy random data is built, and is called the fuzzy random regression model (FRRM). First, a general fuzzy regression model for fuzzy random data is introduced. Then, using expectations and variances of fuzzy random variables, a-confidence intervals are constructed for fuzzy random input-output data. The FRRM is established based on the a-confidence intervals. The proposed regression model gives rise to a non-linear programming problem which consists of fuzzy numbers or interval numbers. Since sign-changes in the fuzzy coefficients modify the entire programming structure of the solution process, the inherent dynamic non-linearity of this optimization makes it hard to exploit the techniques of linear programming or classical non-linear programming. Therefore, we resort to some heuristics. Finally, an illustrative example is provided.

The primary objective of clustering is to discover a structure in the data by forming some number of clusters or groups. In order to achieve optimal clustering results in current soft computing approaches, two fundamental questions should be considered; (i) how many clusters should be actually presented in the given data, and (ii) how real or good the clustering itself is. Based on these two fundamental questions, almost clustering method needs to determine the number of clusters. Yet, it is difficult to determine an optimal number of a cluster group should be obtained for each data set. Hence, DNA-based clustering algorithms were proposed to solve clustering problem without considering any preliminary parameters such as a number of clusters, iteration and, etc..
Because of the nature of processes between DNA-based solutions with a silicon-based solution, the evaluation of obtained results from DNA-based clustering is critical to be conducted. It is to ensure that the obtained results from this proposal can be accepted as well as other soft computing techniques. Thus, this study proposes two different techniques to evaluate the DNA-based clustering algorithm; either it can be accepted as other soft computing techniques or the results that obtained from DNA-based clustering are not reliable for employed.

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GA-FCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

A new class of fuzzy stochastic optimization models-two-stage fuzzy stochastic programming with Value-at-Risk (FSP-VaR) criteria is built in this paper. Some properties of the two-stage FSP-VaR, such as value of perfect information (VPI), value of fuzzy random solution (VFRS), and bounds of the fuzzy random solution, are discussed.
An Approximation Algorithm is proposed to compute the VaR by combining discretization method of fuzzy variable, random simulation technique and bisection method. The convergence of the approximation algorithm is proved. To solve the two-stage FSP-VaR, a hybrid mutation-neighborhood-based particle swarm optimization (MN-PSO) which comprises the Approximation Algorithm is proposed to search for the approximate optimal solution. Furthermore, a neural network-based acceleration method is discussed. A numerical experiment illustrates the effectiveness of the proposed hybrid MN-PSO algorithm. The comparison shows that the hybrid MN-PSO exhibits better performance than the one when using other approaches such as hybrid PSO and GA. (C) 2010 Elsevier B.V. All rights reserved.

Portfolio selection problems in investments are most studied in modern finance because of their computational intractability. The basic topic of modern portfolio theory is the way in which investors can construct a diversified portfolio of financial securities so as to achieve improved tradeoffs between risk and return. In this paper, a heuristic algorithm using particle swarm optimization (PSO) is applied to the problem. PSO realizes the search algorithm by combining a local search method through self-experience with global search method through neighboring experience, attempting to balance the exploration trade-off which achieves the efficiency and accuracy of an optimization. A newly obtained effect is proposed in this paper by adding the mutation operator of genetic algorithms (GA) to unravel the stagnation and control the velocity. We applied our adaptation and implementation of the PSO search strategy to the portfolio selection problem. Results on typical applications demonstrate that the velocity information and mutation operator play pivotal roles in searching for the best solution, and that our method is a viable approach for the portfolio selection problem.

A video tracking system. has many potential applications, particularly in security, monitoring and robotics. The most important problem. in tracking systems is object motion tracking. In this paper, we present a new method that combines footstep prediction and particle filter to manage some problems inherent in manipulating a large View image space. We compare various methods and evaluate their capabilities.

The objective of a fuzzy regression model is to illustrate the potentialpossibilities of the target system. In the interval fuzzy regression model,system possibilities are measured by the width of the interval indicated by themodel. If the center of the model coincides with the center of the possibilitydistribution, more information could be obtained about the possibilities of thetarget system. As the model should be intuitive, this would make the proposedmodel more user-friendly and broadly applicable. In this paper, we use thepopulation of a district and its number of households as a numerical example,and we use a basin area and a drift distance in Japan as an application of ourmodel to check its features. ICIC International © 2010.

Based on time-varying production function with FDI path-converged approach,this research not only identifies the regional discrepancy of technicalefficiency and scale efficiency, but also simulates the change of productionefficiency with the changes of technical efficiency and technical efficiency inChinese regions. First, the identification illustrates FDI path producesincreasing technical efficiency and avoids the diminishing marginal returns inEastern region, while both cases fail in both Middle and Western regions.Second, the transplantation of Eastern technical efficiency into Middle regionnot only succeeds in producing output growth, but also keeps the productionefficiency from decreasing. Third, the transplantation of scale efficiency onlysucceeds in producing output growth while fails in reversing the declining trendof production efficiency. ICIC International © 2010.

Most of previous researches have been presented to compute and analyze technical efficiency from economics and statistics perspectives. However, none of any application-based research to realize technical efficiency from management engineering perspective was found in literatures. This paper establishes an innovative Path-convoyed design technique not only to identify technical efficiency but also to realize technical progress in different regions. The FDI path identifies technical level increasing trend in Eastern region and declining trend in both Middle and Western regions in China during 1996-2005. TFP growth is mainly attributed to technical progress rather than efficiency improvement in both Eastern and Middle regions. Most importantly, the innovative realizations of technical progress are obtained in Middle and Western provinces with more than 0.8 and 0.65 times of national GDP per capita, respectively.

The objective of this paper is to build a real options model under hybrid uncertain environment of randomness and fuzziness. In order to well describe the real uncertain situation, we utilize fuzzy random variable as a tool to characterize future cash flows, and propose a new real options analysis approach by combing binomial lattice-based model with fuzzy random variable, as named fuzzy random real options analysis (FR-ROA). Then the proposed FR-ROA is applied to an R&D project problem under fuzzy random environment, and the relations of FR-ROA with the classical ROA and the fuzzy ROA are explicitly discussed, respectively.

Measurement of quality is an important task in the evaluation of agricultural products and plays a pivotal role in agricultural production. The inspection process normally involves a visual examination according to the ripeness standards of crops, and this grading is subject to expert knowledge and interpretation. Therefore, the quality inspection process of fruits needs to be conducted properly to ensure that high-quality fruit bunches are selected for production. However, human subjective judgments during the evaluation make the fruit grading inexact. The objectives of this paper are to build a fuzzy hierarchical evaluation model that characterises the criteria of oil palm fruits to decide the fuzzy weights of these criteria based on a fuzzy regression model, and to help inspectors conduct a proper total evaluation. A numerical example is included to illustrate the computational process of the proposed model.

The failure of effective information management over the web does not influence only on by the speed of information retrieval
but also influenced by individual and organizational activities. Thus, the search engine of information retrieval plays a pivotal role in retrieving results that are relevant to users' query. Categorization is an optimist alternative to improve the accuracy and speed of information retrieval. This paper proposes a method using evidential reasoning based on similarity for text categorization. Term and frequency of term has influenced the process of deciding the document category. In order to define a new category for new document, our proposed method has taking degree similarities into consideration. ©2010 TSI Press.

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge of decision rules from a database, a web base, a set and so on. The decision rules are used for data analysis as well. Then we can reason for an unknown object using the decision rules. In this paper, we apply the rough set theory to analysis of time series data. We acquire knowledge based on one-minute chart fluctuations.

Although advanced information technologies provide useful information to medical service workers, the workers in medical workplaces are offered no easy opportunities for enhancing relationships for the exchange of medical information. One reason for this is that it is difficult to find optimal relationships when a number of medical service workers are involved. In this article, we specifically focus on density analysis of medical service teams where medical service workers are connected via their medical work-related values. We apply a DNA computing method as a profound method by which to find optimal relationships for medical communications. The results of the density analysis show how efficient a DNA computing method approach can be in building a reinforced medical network system.

City renaissance has played an increasingly important role in urban regeneration since the mid-1980s. The concept of the Creative City, proposed by Charles Landry is driving the imagination of city redevelopers. Recent developments have focused less on capital projects and more on the ability of activity in the arts to support community-led renewals. It is essential for researchers to pay more attention to the issue of Creative City development. According to UNESCO, the Creative Cities Network connects cities that will share experiences, ideas, and best practices aiming at cultural, social and economic development. It is designed to promote the social, economical and cultural development of cities in both the developed and the developing world. However, Creative City design must be integrated with a wide range of knowledge and a diverse database. The application of urban development is a complex and delicate task. It involves multiple issues including engineering, economics, ecology, sociology, urban development, art, design and other domains. In order to empower efficiency in concurrent city development, appropriate evaluation and decision tools need to be provided. Building a decision support system of Creative City development can help decision-makers to solve semi-structured problems by analyzing data interactively. The decision support system is based on a new approach to treating rough sets. The method will play a pivotal role and will be employed dynamically in the DSS. The approach realizes an efficient sampling method in rough set analysis that distinguishes whether a subset can be classified in the focal set or not. The algorithm of the rough set model will be used to analyze obtained samples. In this paper we will first examine the design rules of Creative City development by urban design experts. Second, we will apply rough set theory to select the decision rules and measure the current status of Japanese cities. Finally, we will initiate a prototype decision support system for Creative City design based on the results obtained from the rough sets analysis. © Springer-Verlag Berlin Heidelberg 2010.

The chapter aims first to identify existing population agglomeration and its efficiency, and second to simulate decision-making for the optimal migration strategy in urban planning to eliminate inefficiency among cities in China. First, identification based on path-converged design reveals inefficiency in existing population agglomeration in China because the population mostly agglomerates to cities with urbanization levels lower than 0.35 and the population gathers into areas with urbanization levels lower than the average level in large, medium and small cities from both regional and urban perspectives. Second, decision-making for regional population migration performs well in eliminating inefficiency. By emigrating about 14, 10, and 14 percents of the regional population from cities at low urbanization levels to cities at higher urbanization levels, inefficiency strengths between benchmark and regional population distributions shrink to 0.058, 0.041, and 0.056 from 0.1464, 0.0985, 0.1397 for small, medium, and large cities, respectively. © Springer-Verlag Berlin Heidelberg 2010.

From a historical perspective, two fundamental issues are observed for industrial designers: (1) what is the shape design process within the context of a modern product design process, and (2) how shape design theories, methods, tools and computer aided software can be effectively utilized for creating product shapes. A framework is proposed to resolve the issues by describing the relationships of the product design problems, product design processes, shape design processes, shape design methods and tools with consideration of the functional, ergonomic, emotional and manufacturing requirements. The framework implemented here is a new type of decision support system (DSS) - an object-oriented decision support system to assist the designers in designing product shapes. A scooter case illustrates the usage of the framework and the implementation of DSS. According to the planned design process and design method, a shape grammar is used as knowledge representation and knowledge reasoning method for creating scooter shapes. The functional and ergonomic requirements can be explicitly expressed in the shape grammar. The designer can interactively apply the shape grammar with consideration of emotional and manufacturing requirements. Accordingly, the industrial designers can use a DSS to plan their own shape design processes and utilize the shape design tools, methods, knowledge for their own design problems without practicing the complicated and interdisciplinary knowledge of the shape design for many years. © Springer-Verlag Berlin Heidelberg 2010.

In this study, we present a new idea dealing with the analysis of fuzzy random variables (FRVs) being treated as samples of data. The proposed concept can be used to model various real-life situations where uncertainty is not only present in the form of randomness but also comes in the form of imprecision described in terms of fuzzy sets. We propose a hybrid approach, which combines a convex hull approach (called Beneath-Beyond algorithm) with a fuzzy random regression analysis. Falling under the umbrella of intelligent data analysis (IDA) tool, this approach is suitable for real-time implementation of data analysis. For a fuzzy random data set, we include simulation results and highlight two main advantages, namely a decrease of required analysis time and a reduction of computational complexity. This emphasizes that the proposed IDA approach becomes an efficient way for real-time data analysis. © 2010 IEEE.

Due to subjective judgement, imprecise human knowledge and perception in capturing statistical data, the real data of lifetimes in many systems are both random and fuzzy in nature. Based on the fuzzy random variables that are used to characterize the lifetimes, this paper studies the redundancy allocation problems to a fuzzy random parallel-series system. Two fuzzy random redundancy allocation models (FR-RAM) are developed through reliability maximization and cost minimization, respectively. Some properties of the FR-RAM are obtained, where an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed. To solve the FR-RAMs, we first address the computation of reliability. A random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes. As for the reliability with nonconvex lifetimes, the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability, and a convergence theorem of the fuzzy random simulation is proved. Subsequently, we integrate the computation approaches of the reliability and genetic algorithm (GA) to search for the approximately optimal redundancy allocation of the models. Finally, some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness. © 2010 Springer-Verlag Berlin Heidelberg.

This paper attempts to estimate the coefficient of the goal constraints through a fuzzy random regression model which plays a pivotal role in solving a stochastic fuzzy additive goal programming. We propose the two phase-based solutions; in the first phase, the goal constraints are constructed by fuzzy random-based regression model and, in the second phase, the multi-objective problem is solved with a stochastic fuzzy additive goal programming model. Further, we apply the model to a multi-objective decision-making scheme's use in palm oil production planning and give a numerical example to illustrate the model.

The objective of this work is to provide, as a search engine, latent semantic indexing (LSI), which is a classical method to produce optimal approximations of a term-document matrix and has been used for textual information mining. The use of this technique is examining mine content which based web document, using keyword features of documents. Experimental results show that together with both textual and latent features LSI can extract the underlying semantic structure of web documents, thus improve the search engine performance significantly.

Video tracking can be defined as an action which can estimate the trajectory of an object in the image plane as it moves within a scene. A tracker assigns consistent labels to the tracked objects in different frames of a video. The objective of this paper is to provide information on the present state of the art and to discuss future trends in the use of multi-camera tracking systems. In the literature, three main types of multi-camera tracking system have been outlined. The first type relies on challenges in the camera tracking system. The second concerns the methodology of tracking systems in general. The third type relies on current trends in camera tracking systems. We provide an overview of the current research status by summarizing promising avenues for further research.

Path-converged design is applied to realize the efficiency improvement ofpublic expenditure's allocation. Different from previous researches, first, itestablishes an efficiency-oriented framework rather than the input-oriented oroutput-oriented one. Second, it allows us to identify allocation efficiency ofpublic expenditure from a perspective of distribution, rather than a perspectiveof parameters of production efficiency. Third, it realizes efficientimprovement via increasing inputs in an inefficient DMU instead of decreasinginputs. The successful realization of inefficiency strengths to 2 percents isobtained by reallocating 2.02 percents or additionally investing 7.38 percentsof original public expenditure in overall China. ICIC International © 2009.

There are many forecasting techniques including the ARIMA model, GARCH model, exponential smoothing, neural networks, genetic algorithm, etc. Those methods, however, have their drawbacks and advantages. Since financial time series may be influenced by many factors, such as trading volume, business cycle, oil price, and seasonal factor, conventional model based on prediction methodologies and hard computing methods seem inadequate. In recent years, the innovation and improvement of forecasting methodologies have caught more attention, and also provide indispensable information in the decision-making process, especially in the fields of financial economics and engineering management. In this paper, a new forecasting methodology inspired by natural selection is developed The new forecasting methodology may be of use to a nonlinear time series forecasting. The method combines mathematical, computational, and biological sciences, which includes fuzzy logic, DNA encoding, polymerase chain reaction, and DNA quantification. In the empirical study, currency exchange rate forecasting is demonstrated. The Mean Absolute Forecasting Accuracy method is defined for evaluating the performance, and the result comparing with the ARIMA method is illustrated.

The objective of this paper is to realize an efficient sampling method in. a rough set approach that distinguishes whether a subset can be classified in. the focal set or not. The algorithm of rough set model will be used to analyze samples obtained and in order to illustrate the method, we use some artificial data in this paper. As its application, we discuss public art and urban development. Also, the concept of the Creative City, proposed by Charles Landry, will be reviewed for urban innovators, policy makers. scientists and artists. City renaissance has played an increasingly important role in urban regeneration since the mid-1980s. However, recent developments have focused less on capital projects, and more on the arts activity to support community-led renewals. It is essential for researchers to pay more attention to the issue of the Creative City development. In this paper, the differentiation is examined whether the cities can be classified into a Creative City or not.

This paper proposes an algorithm for probabilistic production cost credit evaluation of wind turbine generators (WTG). Renewable energy resources such as wind, solar, micro hydro, tidal and biomass etc. are becoming increasingly important because of the increased interest in protecting the environment. Wind energy is one of the most successfully used renewable energy sources used to produce electrical energy. The proposed approach was implemented on a power system that includes WTGs. Test results demonstrates the viability of the proposed approach for assessing the wind speed credit from the economics view point.

Today, many Companies contribute to Official Development Assistance (ODA). The details of ODA are publicised for each project, with contributions and companies listed on the company's website. The objective of this paper is to evaluate these companies in terms of the best practices that characterise ODA donations in four fields: infrastructure, medical service, education and security.
Analytic hierarchy process (AHP) is proposed by T. L. Saaty to evaluate uncertainty in decision problems. Using comparison matrices, the AHP can evaluate the extent of data fit to practical data. In fuzzy analytic hierarchy process (Fuzzy AHP), interval weights play a pivotal role and can be solved by linear programming. An approach to fuzzy AHPs is to estimate interval priorities on the items discussed. These interval weights are based on the concept of possibility and express the range of the possibility. The method employed by H. Tanaka is to minimise the range including the given data. The range of the possibility can be illustrated as the interval or width of calculated values. Similar to a conventional AHP model, the fuzzy AHP model has a hierarchical structure that is used to decide the priority of each alternative with the minimal evaluation width of each alternative. Saaty proposed that an AHP matrix should have a Consistency Index (C. I.) of less than 0.1, since theory suggests that C.I. should be satisfied. The fuzzy AHP Model can. minimise such vagueness and uncertainty in the hierarchical structure used to evaluate alternatives.

In this paper, a new and systematic approach for the integration of fuzzy-based methods and biological computation, named as an evolutionary and biological method, is proposed for searching cliques in a fuzzy graph. When dealing with a number of nodes in a graph, the most intractable problem is often detecting the maximum clique, which is automatically obtained from finding, a solution to the arranged cliques in descending order. The evolutionary and biological method is proposed to identify all the cliques and to arrange them in a fuzzy graph, and then to structure all the nodes in the graph, based on the searched cliques, in different hierarchical levels. This challenging approach, involving the integration of two techniques, provides a new and better method for solving clique problems.

Transportation plays a pivotal role in our society, especially in a good quality of life and economic prosperity. Intelligent transportation system (ITS) has been developed to manage the transport infrastructure and vehicles since the number of vehicles is rapidly growing and to avoid any accident. Various applications have provided to support ITS. One of them is a driver-assistant system. Considering of heavy vehicles such as bus, truck, trailer and etc., the driver assistant system is of importance in monitoring and recognizing objects in vehicle surrounding. For example, in operating a heavy vehicle, a driver has a limited view of the vehicle surrounding itself. It is difficult for the driver to ensure that the surrounding of vehicle is safe before operating the machine. Thus, in this paper, we employ a video tracking system through PSO and Parzen particle filter to break through several problems such as simultaneous motion and occlusion among objects. This method makes it easy to track a human movement from every frame and indirectly require less a processing time for tracking an object location in a video stream compared to conventional method. The detail outcome and result are discussed using experiments of the method in this paper.

A huge number of different product types are managed through various processes in facilities with different approaches to scheduling. In this paper, we concentrate mainly on process-focused facilities. Sample groups of such facilities and processes were selected: its orders and times were investigated using both biological computation and genetic algorithms. First, biological computation was used to determine practical schedules. Second, genetic algorithms were used to identify which of the schedules determined by biological computation worked best. Here, we examine how combining these methods can be applied to solving process-focused scheduling problems.

Many of the applied methods and measurement tools of emotional usability engineering have been recommended for use designing products. A rough set method can also be a useful tool to be integrated with the basic concepts of emotional usability engineering. If such a method is applied, the groups of sensory words have to be investigated and their values reduced and classified to provide comprehensive information to product designers. However, a computational problem exists regarding the number of samples, groups of sensory words, and values required when resolving sense-based minimal decision rules. Considering this problem, we discuss the use of DNA computing, and propose a bio-inspired evolutionary method based on the rough set method, which should provide a new tool for emotional usability engineering.

In this paper, using Value-at-Risk, a new fuzzy portfolio selection model named VaR-FPSM is proposed. The Value-at-Risk is the measure of risk, which describes the greatest loss of an investment with some confidence level. When security returns are same kind of fuzzy variable, we derive two crisp equivalent forms of the VaR-FPSM. Furthermore, in general situations, we designed a fuzzy simulation based particle swarm optimization (PSO) algorithm to find an approximately optimal result. To illustrate the proposed model and hybrid PSO algorithm, a numerical example is provided and some discussions on the results are given.

In complicated industrial and organizational relationships between employees or workers. it is difficult to offer good opportunities for their psychological and skill growth, since our progressive information and industrial societies have created many menial tasks. Redesigning subgroups in a personnel network for work rotation is a method that organizes employees appropriately to address these types of problems. In this article, we focus on a fuzzy density analysis of subgroups where employees are connected via their relationships with fuzzy values. However, it becomes extremely hard to rearrange those employees when there are vast numbers of them, meaning it is an NP-hard problem. In the personnel network, all the possible cohesive subgroups can be detected by making the best use of DNA oligonucleotides. which is also applied as a method by which to rearrange employees via fuzzy values based on the results of a fuzzy density analysis.

In social science research, many decisions, evaluations, or psychological test is performed using surveys or questionnaires to seek people's opinion. But some market decisions are always executed by experts. It may lose some information on a real consumers market because of human subjective recognition. In this paper we propose a fuzzy synthesis evaluation on market survey. We propose some fuzzy questionnaires of the 19th Taiwan Golden Melody Awards (TGMA) and ask people answer them in fuzzy logic. The primary goal is that we can reduce the calculation of the evaluation process and thoroughly understand everyone's thought about pop music. Moreover, we hope that this evaluation process can be used on internet and easily enable us to reach decision based on population in the future.

Usually it is hard to classify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables and statistical t-test to restructure a rough set. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

In a clustering analysis, the main problem is often referred to the uncertainty of the data, which could be possibly clustered, meaning the quality of the designed, improved, or analysed system that could be evaluated by this uncertainty of the clustered data. A reliable optimal solution from clustering data could be found by making the best use ofDNA computing. In this paper, a reliable optimal algorithm is proposed to cluster specific data for supporting a complicated data structure based on DNA computing with applied statistics. Its realization is very challenging while the underlying goal could be easily understood in a dimensional space. Given their nature, clustering problems become NP-complete problems. The use of DNA computing as a vehicle of data clustering with applied statistics is discussed and described in this study.

A bio-inspired evolutionary method with DNA is presented for solving a cable trench problem in this paper. The cable trench problem is a combination of the shortest path problem and the minimum spanning tree problem, which makes it difficult to be solved by a conventional computing method. DNA computing is applied to overcome the limitation of a silicon-based computer. The numerical values are represented by the fixed-length DNA strands, and the weights are varied by the melting temperatures. Biochemical techniques with DNA thermodynamic properties are used for effective local search of the optimal solution.

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge as a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can reason an unknown object using the decision rule. The objective of this paper is to apply the rough set theory to analysis of time series data. It is possible to acquire knowledge from time series data using regression line and apply a method to predictions..

Project management is one of central issues in management of technology and engineering. Kathy Schwalbe summarized recent statistics that average time overrun is 163% in 2001 and 227% in 1995, and average cost overrun is 145% in 2001 and 189% in 1995. The project management is not so much successful. A software development company is expected to provide high quality and functional software to the world. It is difficult to control software quality because software is invisible and cannot be felt with our finger. In addition, it is hard to make its productivity efficient because software is created as a result of brainwork. Generally speaking, a project team is organized to create software. Human relationships and work environment affect software qualities. Therefore, it is very important to study the project management of software development for the cost of management and the quality control. The objective of this paper is to illustrate the influential features of software development projects by analyzing questionnaires collected from several software development companies. In the analysis, a multivariate model is employed to quantitatively evaluate the influential features of software development projects.

It is difficult to recognize the state of a human body. In this paper we employ biometrics in order to recognize human behavior. Information obtained form human bodies such as temperature, blood pressure, α brainwave and etc. can be employed to recognize the state of a human body, in other words, the human behaviors. The objective of this paper is to achieve the recognition of the state whether a human is active or relaxed by his biometrics. It is required to recognize the aged person's behavior.

A library provides us with books, journals, news papers and so on according to the demand of its users. The selection of books to buy is also done based on the mission of each library. Usually, they purchase books according to their object as a city, an institute, a school, a university, etc. are founded. It is more important to install books which users are requiring. In this paper we provide a method how to adjust selection of books to the demand of users. The objective of the paper is to propose a method for strategic decision of selecting books. A method is also to deal with latitude in an aspiration level of the decision maker according to their mission. In this method, a librarian and a manager define, for each of expected utility rate and its variance, a necessity level and a sufficient level of users. And then, they can obtain a solution that satisfies an aspiration level of the users. We employ this method to analyze and decide which kind of books and how much rate of the total budget should be spent to buy, where the method of fuzzy mean-variance analysis is employed to solve the problem.

DNA computing approach has gained wide interest in recent years since Adleman shows that the technique can be used to solve the Hamiltonian Path Problem (HPP). Since then there has been many research results showing how DNA computing is used to solve a variety of similar combinatorial problems which is mainly in the realm of computer science. However, the application of DNA computing in solving engineering related problems has not been well established. In this paper we demonstrate how DNA computing can be used to solve a two-elevator scheduling problem for a six-storey building. The research involves finding a suitable technique to represent the DNA sequences in finding the optimal route for each elevator based on initial conditions such as the present position of the elevator, the destination of passengers in the elevators and hall calls from a floor. The approach shows that the DNA computing approach can be well-suited for solving such real-world application in the near future.

In this paper we Will analyze data obtained from a fuzzy set, where samples are defined using a membership grade of a fuzzy set. Generally, multivariant model is to analyze samples characterized using plural variants. In this paper such smaples are also characterized by a fuzzy set. Our aim is to build fuzzy discriminant model and fuzzy pattern classification model.

It is important that the limited amount of investing funds should be efficiently allocated to many stocks so as to reduce its risk. This problem is formulated as a mixed integer programming problem. However, it is not so easy to solve the mixed integer programming problem because of its combinatorial nature. Therefore, an efficient approximate method is required to solve a large-scale mixed integer programming problem. In this paper we propose a Meta-controlled Boltzmann machine to obtain an approximate solution of the large-scale mixed integer programming problem.

The objective of this paper is to employ Demster-Shafer theory into analyzing the hierarchical and logical structure with logical operations AND & OR such as a fault tree. One of main differences of the Dempster-Shafer theory from Baysian one is that it enables us to take the lack of knowledge or information into account in its analyses. The considered set of hypothesis must be mutual exclusive and exhaustive in the Dempster-Shafer theory. In this paper, we define state patterns in order to satisfy the mutual exclusiveness and exhaustiveness. The method using state patterns requires us much computational time to solve the evidential reasoning. In order to overcome this difficulty, we also propose the improved method in terms of logical relations of the events in the fault tree.

When we decide the production of each agricultural product in the uncertain circumstance where its sell price. climate, consumers' preference and so on are not known previously, we should take its risk as well ass the best expected selling volume or its interest into consideration. In this paper we employ fuzzy mean-variance analysis to decide the optimal solution in agricultural management which minimizes the risk and maximizes the expected selling volume.
The mean-variance analysis is proposed by H. Markowitz and widely employed in stock investment. Nevertheless. it. is also hard to treat an aspiration beheld by a decision-maker, because the formulation given by H. Markowitz is written using constant rigid values.
In this paper a fuzzy number is employed to deal with a vague aspiration level and a fuzzy goal given by a decision-maker, that is, the fuzzy mean-variance analysis enables us to obtain the best solution which iu realized within a vague aspiration level and a fuzzy goal. In the method, the decision maker defines a necessity level and a sufficient level for each of expected interest rate and risk. The decision-maker can obtain a solution that satisfies an aspiration level and a fuzzy goal required.
The effectiveness of our method is shown using a numerical example.

In order to reduce the computation time in building fuzzy regression model, we propose the effective convex hull method in 1999 to decrease drastically the number of samples which are not influencial in terms of We employ a fuzzy regression analysis in building a gift-rapping method. Recently multivariant methods play a central role in data mining. In the data mining, real time data analysis is a general approach in nature. In the area, the convex hull method can provide us with a powerful tool to reduce its computational time drastically. In this paper it is explained that (1) only vertex points obtained by the convex hull are constraints in a linear programming of building a fuzzy regression model, that(2) the convex hull approach works efficiently in real-time data gathering, that (3) the number of vertexes obtained by the convex hull will not increase so much even in the real time procedure, that (4) on real time arrived data can be effectively and efficinetly treated by the convex hull method in fuzzy regression analysis, that (5) the method does not consume the duplicated computation of the past analysis, and that (6) the analysis can be built on the basis of the past one using newly arrival data.

Generally when human experts express their ideas and thoughts. human words are basically employed in these expressions. It should be useful to employ fuzzy regression analysis in handling human words and finding the latent structures under these human words. a linguistic regression model is formulated in terms of fuzzy regression analysis and vocabulary matching on the basis of fuzzy numbers.

A project is widely employed in the modern organization of management. The project organization is appropriate to a speed and agile management in uncertain and most changeable economic circumstance today. We propose the method to select members of a new project and distribute budget to each of members based on their past achievement and competencies. The method is named project portfolio management because it takes its risk into consideration.

In the analysis of medical and economic data, it is important to precisely grasp the present state of their systems. These human systems such as economical system or human bodies are influenced by many uncertain factors. Therefore, the systems are modeled as a complex one. It is not sufficient to understand time-series data by means of statistical methods and most important to analyze and understand the data in terms of the concept of fuzziness. In this paper, we employ the fuzzy concept into modeling time-series data and formulate fuzzy AR regression analysis.

Recently, the chaotic method is employed to forecast a short-term future using uncertain data. This method is feasible by restructuring the attractor of given time-series data in the multi-dimensional space through Takens' embedding theory. Nevertheless, it is hard to obtain data which comes only from a chaotic source. Ordinarily, many uncertain time-series data do not come only from a chaotic source, but also from another source. In this paper, we employ related information in order to remove the influence of the non-chaotic source from the given data. This method makes forecasting precision higher because the chaotic portion of the given data can be easily abstracted. In the end, the effectiveness and usefulness of our method are shown by application to a short-term forecasting simulation of Nikkei mean data of the Tokyo stock market.

In the case where data which come out of several different systems are analyzed by a regression model, we should separate some partition of data which come from one system and analyze each of partitions by a regression model in the proper way. Bezdek has proposed a switching regression model based on cluster analysis and regression analysis. In this study, we propose the genetic model of fuzzy switching regression model based on a fuzzy regression model.

In this paper, a fuzzy concept is employed to construct a principal component model which can deal with fuzziness, vagueness or possibility of a system, which is named a possibilistic principal component analysis. The principal component analysis is to analyze a possibility of fuzzy numbers. The possibilistic principal component analysis has three formulations according the portions which the possibilities included in fuzzy numbers are embodied : 1) an eigenvalue, 2) an eigenvector and 3) both eigenvalue and eigenvector. In this paper, we discuss about the first formulation 1) that an eigenvalue is employed to deal with fuzziness of data. The possibilistic principal component analysis is employed in this paper to analyze the features of information technology industry. In this analysis, the financial ratio is employed as indices. And we evaluate the possibility of a company activity in information technology industry.

In a fuzzy controller, the rules derived from experts and the parameters of a membership function which correspond to a verbal word play an important role. The rule acquisition and the auto-tuning of membership parameters are one of prevalent research fields. In this paper, we propose a tuning method of the membership parameters for the fuzzy controller. The evaluation function of the fuzzy controller is well known as a very complex multi-peaked one against these membership parameters. The auto tuning method proposed here employs genetic algorithm in order to search for optimum solution which avoids falling in local optimum. The effectiveness and efficiency of the proposed method are ensured using a simulated example. Copyright (C) 1998 IFAC.

A set of qualitative data obtained by rating a product usually has a large variance reflecting tastes and preferences of individuals. It is sensible to express such fluctuations by fuzzy numbers to treat vagueness and unvertainty of the feeling of individuals. This paper proposes a factor analysis technique for fuzzy data which are rating scores measured by words that are mainly adjective such as innovative, bright, elegant or cheerful. Fuzzy correlation coefficients are introduced and factor loadings are determined as fuzzy numbers through linear programming. Thus, words are identified as fuzzy objects in the factor space. After fuzzy distances between words in the factor space are defined, a covering problem is formulated as an integer programming problem to determine a set of representative words and an overlapped partition of words simultaneously. This provides a useful information to study the relation between words and design elements.