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Fuzzy Learning Method Using Genetic Algorithms
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  • Fuzzy Learning Method Using Genetic Algorithms
  • Fuzzy Learning Method Using Genetic Algorithms
저자명
Choi. Sangho,Cho. Kyung-Dal,Park. Sa-Joon,Lee. Malrey,Kim. Kitae
간행물명
멀티미디어학회논문지
권/호정보
2004년|7권 6호|pp.841-850 (10 pages)
발행정보
한국멀티미디어학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

This paper proposes a GA and GDM-based method for removing unnecessary rules and generating relevant rules from the fuzzy rules corresponding to several fuzzy partitions. The aim of proposed method is to find a minimum set of fuzzy rules that can correctly classify all the training patterns. When the fine fuzzy partition is used with conventional methods, the number of fuzzy rules has been enormous and the performance of fuzzy inference system became low. This paper presents the application of GA as a means of finding optimal solutions over fuzzy partitions. In each rule, the antecedent part is made up the membership functions of a fuzzy set, and the consequent part is made up of a real number. The membership functions and the number of fuzzy inference rules are tuned by means of the GA, while the real numbers in the consequent parts of the rules are tuned by means of the gradient descent method. It is shown that the proposed method has improved than the performance of conventional method in formulating and solving a combinatorial optimization problem that has two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy rules.