SELECTION OF FUZZY IF-THEN RULES BY A GENETIC METHOD

被引：1
|
作者：
ISHIBUCHI, H
NOZAKI, K
YAMAMOTO, N
TANAKA, H
机构：
[1] College of Engineering, University of Osaka Prefecture, Sakai
来源：
ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE | 1994年 / 77卷 / 02期
关键词：
FUZZY CLASSIFICATION SYSTEMS; PATTERN CLASSIFICATION; FUZZY IF-THEN RULES; GENETIC ALGORITHMS;
D O I：
10.1002/ecjc.4430770210
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper proposes a genetic algorithm-based method for constructing a fuzzy classification system with fuzzy if-then rules. In the proposed method, a rule selection problem for constructing a compact fuzzy system with high classification power is formulated as a combinatorial optimization problem with two objectives: to maximize the classification rate and to minimize the number of rules. Then a method of implementing genetic algorithms is proposed for the application to this problem and its effectiveness is demonstrated by computer simulations. The implementation of genetic algorithms in this paper employs an approach where a set of fuzzy if-then rules is coded as a single individual.
引用
收藏
页码：94 / 104
页数：11
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