Neuro-Fuzzy Modeling for Nonlinear System Using VmGA

VmGA를 이용한 비선형 시스템의 뉴로-퍼지 모델링

  • 최종일 (군산대학교 전자정보공학부) ;
  • 이연우 (군산대학교 전자정보공학부) ;
  • 주영훈 (군산대학교 전자정보공학부) ;
  • 박진배 (연세대학교 전기전자공학과)
  • Published : 2001.07.18

Abstract

In this paper, we propose the neuro-fuzzy modeling method using VmGA (Virus messy Genetic Algorithm) for the complex nonlinear system. VmGA has more effective and adaptive structure than sGA. in this paper, we suggest a new coding method for applying the model's input and output data to the optimal number of rules in fuzzy models and the structure and parameter identification of membership functions simultaneously. The proposed method realizes the optimal fuzzy inference system using the learning ability of neural network. For fine-tune of parameters identified by VmGA, back- propagation algorithm is used for optimizing the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through comparing with ANFIS.

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