Genetically Optimized Self-Organizing Fuzzy Polynomial Neural Networks based on Information Granulation and Evolutionary Algorithm

  • 박호성 (원광대학교 공과대학 정보디지털시스템학과) ;
  • 오성권 (수원대학교 공과대학 전기공학과)
  • Park Ho-Sung (Dept. of Information and Digital System Engineering, Wonkwang University) ;
  • Oh Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
  • 발행 : 2005.04.01

초록

In this study, we proposed genetically optimized self-organizing fuzzy polynomial neural network based on information granulation and evolutionary algorithm (gdSOFPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization. The proposed gdSOFPNN gives rise to a structural Iy and parametrically optimized network through an optimal parameters design available within FPN (viz. the number of input variables, the order of the polynomial, input variables, the number of membership functions, and the apexes of membership function). Here, with the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The performance of the proposed gdSOFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling.

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