Fuzzy associative matrix

About: Fuzzy associative matrix is a(n) research topic. Over the lifetime, 8027 publication(s) have been published within this topic receiving 194790 citation(s).

Fuzzy Sets and Systems: Theory and Applications

Abstract: (1982). Fuzzy Sets and Systems — Theory and Applications. Journal of the Operational Research Society: Vol. 33, No. 2, pp. 198-198.

Fuzzy logic in control systems: fuzzy logic controller. II

Abstract: For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated. Defuzzification strategies, are discussed. Some of the representative applications of the FLC, from laboratory level to industrial process control, are briefly reported. Some unsolved problems are described, and further challenges in this field are discussed. >

Generating fuzzy rules by learning from examples

Abstract: A general method is developed to generate fuzzy rules from numerical data. The method consists of five steps: divide the input and output spaces of the given numerical data into fuzzy regions; generate fuzzy rules from the given data; assign a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; create a combined fuzzy rule base based on both the generated rules and linguistic rules of human experts; and determine a mapping from input space to output space based on the combined fuzzy rule base using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. Applications to truck backer-upper control and time series prediction problems are presented. >

A fuzzy-logic-based approach to qualitative modeling

Abstract: This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.

A fuzzy extension of Saaty's priority theory

Abstract: We present a fuzzy method for choosing among a number of alternatives under conflicting decision criteria: a fuzzy version of Saaty's pairwise comparison method (1980) extended by de Graan (1980) and Lootsma (1981).Each ratio expressing the relative significance of a pair of factors is displayed in a matrix, from which suitable weights can be extracted. Since these ratios are essentially fuzzy-they express the opinion of a decision-maker on the importance of a pair of factors-we have adapted the above-mentioned method in such a way, that decision-makers are asked to express their opinions in fuzzy numbers with triangular membership functions. We apply the method at two distinct levels: first to find fuzzy weights for the decision criteria, and second, to find fuzzy weights for the alternatives under each of the decision criteria. By a suitable combination of these results, we obtain fuzzy scores for the alternatives, as well as their sensitivities. Using these fuzzy scores, the decision-makers should be able to make a choice for one of the alternatives.