Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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Design of Hybrid Architecture of Neurofuzzy Polynomial Networks

뉴로퍼지 다항식 네트워크의 하이브리드 구조 설계

  • Park, Byoung-Jun (School of electrical Electronic and information engineering, Wonkwang university) ;
  • Park, Ho-Sung (School of electrical Electronic and information engineering, Wonkwang university) ;
  • Oh, Sung-Kwun (School of electrical Electronic and information engineering, Wonkwang university) ;
  • Jang, Sung-Whan (School of electrical Electronic and information engineering, Wonkwang university)
  • 박병준 (원광대학교 공과대학 전기전자 및 정보공학부) ;
  • 박호성 (원광대학교 공과대학 전기전자 및 정보공학부) ;
  • 오성권 (원광대학교 공과대학 전기전자 및 정보공학부) ;
  • 장성환 (원광대학교 공과대학 전기전자 및 정보공학부)
  • Published : 2001.11.24
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Abstract

In this study, we introduce a concept of neurofuzzy polynomial networks (NFPN), a hybrid modeling architecture combining neurofuzzy networks (NFN) and polynomial neural networks(PNN). NFN contributes to the formation of the premise part of the rule-based structure of the NFPN. The consequence part of the NFPN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We introduce two kinds of NFPN architectures, namely the basic and the modified one. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability.

Keywords