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A genetic algorithm for generating optimal fuzzy rules  

임창균 (여수대학교 컴퓨터공학과)
정영민 (여수대학교 컴퓨터공학과)
김응곤 (순천대학교 컴퓨터과학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.7, no.4, 2003 , pp. 767-778 More about this Journal
Abstract
This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.
Keywords
유전자 알고리즘;신경망;퍼지 신경망;퍼지 추론 시스템;
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