Journal of Intelligent & Fuzzy Systems - Volume 18, issue 2

Authors: Wu, Wann-Yih | Lin, Chinho | Kung, Jung-Yuan | Lin, Chia-Tzu

Article Type: Research Article

Abstract: Fuzzy MADM (multiple attributes decision making) problem has received much attention from researchers since it has been an important issue in numerous fields. Fuzzy TOPSIS (technique for order preference by similarity to ideal solution) has become one of the most widely used fuzzy MADM methods. This paper proposes a new fuzzy TOPSIS to deal with the fuzzy MADM problem under group decisions. This approach employs triangular fuzzy numbers to represent linguistic values for indicating importance weights …of chosen attributes and evaluating alternative candidates under different attributes. This approach can overcome the drawbacks shown in existing related studies, and it can be applied in various domains. A numerical example is also given to demonstrate the proposed approach. Show more

Keywords: TOPSIS, fuzzy MADM, linguistic values

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 109-115, 2007

Authors: Kung, Jung-Yuan | Chuang, Tzung-Nan | Lin, Chia-Tzu

Article Type: Research Article

Abstract: Numerous articles have focused on the fuzzy shortest path problem in a network since it is central to various applications. It purposes to provide a decision maker with the shortest path length and the shortest path in a fuzzy network. In this paper, we propose a dynamic programming approach for fuzzy shortest path problem in a network where each arc length is represented as a discrete fuzzy set. This approach can be used to treat discrete fuzzy shortest …path problem without some shortcomings existing in other related researches. A classic example is also included to demonstrate our proposed approach. Show more

Keywords: Fuzzy shortest path problem, decision maker, dynamic programming

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 117-122, 2007

Authors: Kim, Euntai | Pedrycz, Witold

Article Type: Research Article

Abstract: Fuzzy clustering forms a cornerstone of fuzzy (granular) modeling. The clusters (prototypes) are viewed as a blueprint of the model that is further refined through a number of detailed estimation techniques. In this study, we claim that while clustering is indisputable essential to fuzzy modeling, the essence of clustering mechanisms supporting this process of information granulation is not compatible with the character of the task at hand. In modeling, the required constructs are inherently direction-sensitive …(that is we clearly distinguish between input and output variables). On the other hand, fuzzy clustering is direction neutral and during the formation of the clusters does not take this into consideration. We re-formulate the clustering so that the directionality aspect can be addressed in the optimization process. This leads to a new, augmented objective function to be minimized. A detailed algorithm is derived. As the directional sensitivity of the clustering method gives rise to different numbers of clusters in the input and output space, it becomes necessary to identify a mapping between these clusters which in turn gives rise to some allocation problem. Because of its inherently combinatorial character, the proposed solution is obtained through some genetic optimization. Comprehensive experiments demonstrate the performance of the approach and compare it with some of the generic version of the FCM clustering. Show more

Keywords: Fuzzy clustering, function approximation, genetic algorithm, fuzzy granular modeling, information granulation, fuzzy c-means, directional aspects of clustering

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 123-148, 2007

Authors: Li, Shunqin | Zhao, Ruiqing | Tang, Wangsheng

Article Type: Research Article

Abstract: The purpose of this paper is to introduce a fuzzy random delayed renewal process in which the interarrival times between two events are characterized as fuzzy random variables. The general properties of the process are discussed. In addition, the definition of a fuzzy random equilibrium renewal process, as a special case of the fuzzy random delayed renewal process, is also given. Finally, concerned with the fuzzy random equilibrium renewal process, the expected value of a fuzzy …random equilibrium renewal variable and the mean chance of the remaining life are studied. Show more

Keywords: Fuzzy variables, fuzzy random variables, delayed renewal process, equilibrium renewal process

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 149-156, 2007

Authors: Li, Yun | Lu, Bao-Liang | Wu, Zhong-Fu

Article Type: Research Article

Abstract: The problem of feature selection has long been an active research topic within statistics and pattern recognition. So far, most methods of feature selection focus on supervised data where class information is available. For unsupervised data, the related methods of feature selection are few. The presented article demonstrates a way of unsupervised feature selection, which is a two-level filter model removing the redundant and irrelevant features, respectively. The redundant features are eliminated using …any clustering algorithm, and a new method is proposed to remove the irrelevant features: first rank the features according to their relevance to cluster and then a subset of relevant features is selected using the Fuzzy Feature Evaluation Index (FFEI) with some changes and extensions. The experimental results have shown the effectiveness of the proposed method for high-dimensional data. Our major contributions are: (1) to present a new hierarchical filter method for unsupervised feature selection; (2) to propose a new algorithm for removing the irrelevant features; (3) to extend the FFEI, and present a method for calculating the approximate weight of feature in FFEI, which improves the efficiency and robustness of the method. Show more

Keywords: Unsupervised feature selection, fuzzy set, ranking index, filter method

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 157-169, 2007

Authors: Yu, Wen | Moreno-Armendariz, Marco A. | Rodriguez, Floriberto Ortiz

Article Type: Research Article

Abstract: Hierarchical fuzzy neural networks can use less rules to model nonlinear system with high accuracy. But the normal training method for hierarchical fuzzy neural networks is very complex. In this paper we modify the backpropagation approach and employ a time-varying learning rate that is determined from input-output data and model structure. Stable learning algorithms for the premise and the consequence parts of the fuzzy rules are proposed. The calculation of the learning rate does not need …any prior information such as estimation of the modeling error bounds. The new algorithms are very simple, we can train each sub-block of the hierarchical fuzzy neural networks independently. Show more

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 171-183, 2007

Authors: Manamanni, N. | Mansouri, B. | Hamzaoui, A. | Zaytoon, J.

Article Type: Research Article

Abstract: In this paper, a fuzzy tracking control is designed for nonlinear dynamic systems with external disturbances using a TS fuzzy model and state observer design. A control scheme based on an augmented system with a guaranteed H∞ performance and a model reference tracking is proposed. The aim of this paper is to provide a more relaxed version of the control design problem in terms of a linear matrix inequality (LMI) and to improve the tracking performances. …In the case of a tracking problem, the results obtained are summarized in two theorems concerning the feasibility of the LMI and the relaxed stability conditions. A design example illustrates the relaxed stability conditions and the tracking performances of this approach. Show more

Keywords: Fuzzy control, linear matrix inequality (LMI), quadratic stability, tracking, relaxed condition, H∞ robust control, fuzzy system

Citation: Journal of Intelligent & Fuzzy Systems, vol. 18, no. 2, pp. 185-210, 2007