대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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- Pages.2891-2893
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- 2005
진화론적 파라미터 동정에 기반한 자기구성 퍼지 다항식 뉴럴 네트워크의 새로운 설계
A New design of Self Organizing Fuzzy Polynomial Neural Network Based on Evolutionary parameter identification
- Park, Ho-Sung (School of Electrical Electronic & Information Engineering, Wonkwang Univ.) ;
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Lee, Young-Il
(Dept. of Electrical Engineering, Suwon Univ.) ;
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Oh, Sung-Kwun
(Dept. of Electrical Engineering, Suwon Univ.)
- 발행 : 2005.07.18
초록
In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.
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