A study on nonlinear data-based modeling using fuzzy neural networks

퍼지신경망을 이용한 비선형 데이터 모델링에 관한 연구

  • 권오국 (연세대학교 전기공학과) ;
  • 장욱 (연세대학교 전기공학과) ;
  • 주영훈 (군산대학교 제어계측공학과) ;
  • 최윤호 (경기대학교 전자공학과) ;
  • 박진배 (연세대학교 전기공학과)
  • Published : 1997.10.01

Abstract

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

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