An alternative characterization of fuzzy complement functional

In this short note we propose a novel fuzzy complement functional. This functional is different from other functionals known in the literature. However, it turns out to be an alternative characterization of the well-known negation function. We sincerely thank the anonymous reviewer whose insightful suggestions have significantly improved the paper.     

A fuzzy-controlled single-phase active power filter operating with fixed switching frequency for reactive power and current harmonics compensation

This paper introduces a single-phase voltage source current-controlled pulse width modulation rectifier model, acting as an active power filter (APF), with a fuzzy logic controller in the inner loop and a simple proportional-integral (PI) d.c. link voltage controller in the outer loop to compensate reactive power and current harmonic components simultaneously from nonlinear loads. The proposed scheme operates with a fixed switching frequency. The control scheme of the APF is simple, since it has fewer control inputs for determining the desired action. It is easily tunable for different performance criteria and can be achieved by means of low-cost of hardware installation, as it does not need to use more sensitive and accurate measuring devices. The model can also compensate both reactive and harmonic current components from nonlinear loads.     

A methodology of determining aggregated importance of engineering characteristics in QFD

Quality function deployment (QFD) is a planning and problem-solving tool that is gaining acceptance for translating customer requirements (CRs) into engineering characteristics (ECs) of a product. Deriving the importance of ECs is a crucial step of applying QFD. However, the inherent fuzziness in QFD presents a special challenge to effectively evaluate the importance of ECs and correlation among them. Furthermore, degree of impact of an engineering characteristic (EC) on the other ECs also reflects the importance of the ECs. In previous studies, those impacts were neglected or simply represented using a linear combination in determining the importance of ECs. To address this issue, in this paper, a new methodology of determining aggregated importance of ECs is presented which involves the consideration of conventional meaning of importance of ECs as well as the impacts of an EC on other ECs. In the proposed methodology, fuzzy relation measures between CRs and ECs as well as fuzzy correlation measures among ECs are determined based on fuzzy expert systems approach. These two types of measures are then used to determine the aggregated importance of ECs. An example of design of a digital camera is used to illustrate the proposed methodology.     

A fuzzy neural network model for predicting clothing thermal comfort

This paper presents a Fuzzy Neural Network (FNN) based local to overall thermal sensation model for prediction of clothing thermal function in functional textile design system. Unlike previous experimental and regression analysis approaches, this model depends on direct factors of human thermal response — body core and skin temperatures. First the local sensation is predicted by a FNN network using local body part skin temperatures, their change rates, and core temperature as inputs; then the overall sensation is predicted. This is also performed by a FNN network. The FNN networks are developed on the basis of the Feed-Forward Back-Propagation (FFBP) network; the advantage of using fuzzy logic here is to reduce the requirement of training data. The simulation result shows a good correlation between predicted and the traditional experimental data.     

Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation

Feature subset selection has become an important challenge in areas of pattern recognition, machine learning and data mining. As different semantics are hidden in numerical and categorical features, there are two strategies for selecting hybrid attributes: discretizing numerical variables or numericalize categorical features. In this paper, we introduce a simple and efficient hybrid attribute reduction algorithm based on a generalized fuzzy-rough model. A theoretic framework of fuzzy-rough model based on fuzzy relations is presented, which underlies a foundation for algorithm construction. We derive several attribute significance measures based on the proposed fuzzy-rough model and construct a forward greedy algorithm for hybrid attribute reduction. The experiments show that the technique of variable precision fuzzy inclusion in computing decision positive region can get the optimal classification performance. Number of the selected features is the least but accuracy is the best.     

An α-Fuzzy Goal Approximate Algorithm for Solving Fuzzy Multiple Objective Linear Programming Problems

Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree α to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions.     

High performance I/O architectures and systems

I/O- and data-intensive workloads, as represented by the Grand Challenge problems, multimedia applications, cosmology simulations, climate modeling, and large collaborative visualizations, to name a few, entail innovative approaches to alleviate the I/O (both bandwidth and data access) performance bottlenecks. The advent of low-cost hardware platforms, such as the Beowulf clusters, has opened up numerous possibilities in mass data storage, scalable architectures, and large-scale simulations. The objective of this Special Issue is to discuss problems and solutions, to identify new issues, and to help shape future research and development directions in these areas. From these perspectives, the Special Issue addresses the problems encountered at the hardware, middleware, and application levels, providing conceptual as well as empirical treatments.     

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7]. Shi-Jay Chen was born in 1972, in Taipei, Taiwan, Republic of China. He received the B.S. degree in information management from the Kaohsiung Polytechnic Institute, Kaohsiung, Taiwan, and the M.S. degree in information management from the Chaoyang University of Technology, Taichung, Taiwan, in 1997 and 1999, respectively. He received the Ph.D. degree at the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, in October 2004. His research interests include fuzzy systems, multicriteria fuzzy decisionmaking, and artificial intelligence. Shyi-Ming Chen was born on January 16, 1960, in Taipei, Taiwan, Republic of China. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. From August 1987 to July 1989 and from August 1990 to July 1991, he was with the Department of Electronic Engineering, Fu-Jen University, Taipei, Taiwan. From August 1991 to July 1996, he was an Associate Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1996 to July 1998, he was a Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu, Taiwan. From August 1998 to July 2001, he was a Professor in the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. Since August 2001, he has been a Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He was a Visiting Scholar in the Department of Electrical Engineering and Computer Science, University of California, Berkeley, in 1999. He was a Visiting Scholar in the Institute of Information Science, Academia Sinica, Republic of China, in 2003. He has published more than 250 papers in referred journals, conference proceedings and book chapters. His research interests include fuzzy systems, information retrieval, knowledge-based systems, artificial intelligence, neural networks, data mining, and genetic algorithms. Dr. Chen has received several honors and awards, including the 1994 Outstanding Paper Award o f the Journal of Information and Education, the 1995 Outstanding Paper Award of the Computer Society of the Republic of China, the 1995 and 1996 Acer Dragon Thesis Awards for Outstanding M.S. Thesis Supervision, the 1995 Xerox Foundation Award for Outstanding M.S. Thesis Supervision, the 1996 Chinese Institute of Electrical Engineering Award for Outstanding M.S. Thesis Supervision, the 1997 National Science Council Award, Republic of China, for Outstanding Undergraduate Student's Project Supervision, the 1997 Outstanding Youth Electrical Engineer Award of the Chinese Institute of Electrical Engineering, Republic of China, the Best Paper Award of the 1999 National Computer Symposium, Republic of China, the 1999 Outstanding Paper Award of the Computer Society of the Republic of China, the 2001 Institute of Information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the 2001 Outstanding Talented Person Award, Republic of China, for the contributions in Information Technology, the 2002 Institute of information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the Outstanding Electrical Engineering Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE), Republic of China, the 2002 Chinese Fuzzy Systems Association Best Thesis Award for Outstanding M.S. Thesis Supervision, the 2003 Outstanding Paper Award of the Technological and Vocational Education Society, Republic of China, the 2003 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision, the 2005 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 Taiwan Fuzzy Systems Association Award for Outstanding Ph.D. Dissertation Supervision, and the 2006 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision. Dr. Chen is currently the President of the Taiwanese Association for Artificial Intelligence (TAAI). He is a Senior Member of the IEEE, a member of the ACM, the International Fuzzy Systems Association (IFSA), and the Phi Tau Phi Scholastic Honor Society. He was an administrative committee member of the Chinese Fuzzy Systems Association (CFSA) from 1998 to 2004. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics - Part C, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Journal of Intelligent & Fuzzy Systems, an Editorial Board Member of the International Journal of Applied Intelligence, an Editor of the New Mathematics and Natural Computation Journal, an Associate Editor of the International Journal of Fuzzy Systems, an Editorial Board Member of the International Journal of Information and Communication Technology, an Editorial Board Member of the WSEAS Transactions on Systems, an Editor of the Journal of Advanced Computational Intelligence and Intelligent Informatics, an Associate Editor of the WSEAS Transactions on Computers, an Editorial Board Member of the International Journal of Computational Intelligence and Applications, an Editorial Board Member of the Advances in Fuzzy Sets and Systems Journal, an Editor of the International Journal of Soft Computing, an Editor of the Asian Journal of Information Technology, an Editorial Board Member of the International Journal of Intelligence Systems Technologies and Applications, an Editor of the Asian Journal of Information Management, an Associate Editor of the International Journal of Innovative Computing, Information and Control, and an Editorial Board Member of the International Journal of Computer Applications in Technology. He was an Editor of the Journal of the Chinese Grey System Association from 1998 to 2003. He is listed in International Who's Who of Professionals, Marquis Who's Who in the World, and Marquis Who's Who in Science and Engineering.     

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

Creating comprehensible regression models

In this paper we will present a novel approach to data-driven fuzzy modeling which aims to create highly accurate but also easily comprehensible models. This is achieved by a three-stage approach which separates the definition of the underlying fuzzy sets, the learning of the initial fuzzy model, and finally a local or global optimization of the resulting model. The benefit of this approach is that it allows to use a language comprising of comprehensible fuzzy predicates and to incorporate expert knowledge by defining problem specific fuzzy predicates. Furthermore, we achieve highly accurate results by applying a regularized optimization technique.