• DocumentCode
    315437
  • Title

    Efficient fuzzy modeling and evaluation criteria

  • Author

    Matsushita, S. ; Furuhashi, T. ; Tsutsui, H. ; Uchikawa, Y.

  • Author_Institution
    Nagoya Municipal Ind. Res. Inst., Japan
  • Volume
    1
  • fYear
    1997
  • fDate
    27-23 May 1997
  • Firstpage
    283
  • Abstract
    Hierarchical fuzzy modeling using fuzzy neural networks (FNN) is one of the effective approaches for modeling of nonlinear systems. Decision of antecedent structures of fuzzy models of nonlinear systems is made possible by a combination of FNN and genetic algorithm (GA). The disadvantage of this fuzzy modeling method is that the learning of FNN is time consuming. This paper presents an efficient fuzzy modeling method using simple fuzzy inference. The results of fuzzy modeling are heavily dependent on evaluation criteria. This paper also studies effects of evaluation criteria for the decision of the antecedent structure. Numerical experiments are done
  • Keywords
    fuzzy neural nets; genetic algorithms; inference mechanisms; modelling; nonlinear systems; antecedent structure; evaluation criteria; fuzzy modeling; fuzzy neural networks; genetic algorithm; learning; nonlinear systems; Abstracts; Data handling; Electronics industry; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Industrial electronics; Input variables; Nonlinear systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on
  • Conference_Location
    Adelaide, SA
  • Print_ISBN
    0-7803-3755-7
  • Type

    conf

  • DOI
    10.1109/KES.1997.616921
  • Filename
    616921