Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
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03 Jun 2006
TL;DR: It appears that with non-interval data, typical of the kind of data collected in social science studies, the choice of defuzzification method has no influence on the output of the questionnaires, and significant correlation was found between the statistical outputs and the fuzzy inference outputs.
Abstract: Defuzzification plays an important role in the implementation of a fuzzy system since the crisp value generated best represents the possibility distribution of all possible fuzzy control outputs. The focus of this paper is on comparison of several defuzzification strategies in two fuzzy inference systems designed to analyze questionnaires. Two different questionnaires were analyzed, one having two fuzzy rules and one having three fuzzy rules for the inference component. The output of Centroid, Bisector, Mean of Maximum (MOM), and Largest of Maximum (LOM) defuzzification methods were compared with the output of a conventional statistical analysis. Significant correlation was found between the statistical outputs and the fuzzy inference outputs. It appears that with non-interval data, typical of the kind of data collected in social science studies, the choice of defuzzification method has no influence on the output. As is suggested in the literature, this may be due to the match between the properties of the various defuzzification methods and the application.
44 citations
TL;DR: By aggregating conjugate fuzzy implications it is shown that an I"A fuzzy implication can be preserved by action of an order automorphism and a family I of fuzzy implications is introduced.
43 citations
TL;DR: Both the fuzzy system and a simple analysis of the weight vectors are enough to discern the hidden knowledge learnt by the neural network.
43 citations
01 Jun 1999
TL;DR: An approach of designing adaptive fuzzy controllers is presented to systematically develop efficient and effective rules for fuzzy controllers to show the effectiveness of the proposed approach.
Abstract: In this paper, an approach of designing adaptive fuzzy controllers is presented to systematically develop efficient and effective rules for fuzzy controllers. The proposed fuzzy controllers are first designed with two basic fuzzy if-then rules. Then according to the design requirements of the fuzzy control system, new fuzzy if-then rules are inserted into the rule-base structure of the fuzzy controller. Initially the inserted fuzzy rules are redundant in the sense that the resultant input-output mapping of the fuzzy rules remains intact. After that the parameters of the membership functions for the fuzzy sets of the newly added fuzzy rules are trained on-line to minimize predefined cost functions. Thus, efficient fuzzy controllers can be systematically designed. Simulations for linear, nonlinear, and delayed systems are provided to show the effectiveness of the proposed approach.
43 citations
TL;DR: An extension of the conventional dynamic programming model introduced by Bellman to the fuzzy case is considered and two fuzzy dynamic programming models are developed and converted into algo r performance of these algorithms is compared to two others based on heuristics.
43 citations