• DocumentCode
    2444668
  • Title

    A framework of fuzzy modeling using genetic algorithms with appropriate combination of evaluation criteria

  • Author

    Suzuki, Toshihiro ; Furuhashi, Takeshi ; Tsutsui, Hiroaki

  • Author_Institution
    Dept. of Inf. Electron., Nagoya Univ., Japan
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    1252
  • Abstract
    Fuzzy modeling is a method to describe nonlinear input-output relationships. Genetic algorithms (GAs) have been used with fuzzy modeling for identification of the structure of a fuzzy model and selection of input variables. Users often require fuzzy models that satisfy multiple evaluation criteria. Assignment of appropriate weights on these criteria is one of the key factors for good GA search. In order to give a guideline for assigning the degree of importance to each evaluation criterion for generating fuzzy models, we examined the characteristic of each evaluation criterion
  • Keywords
    fuzzy logic; genetic algorithms; identification; modelling; evaluation criteria; fuzzy modeling; genetic algorithms; input variables; nonlinear input-output relationships; search; Character generation; Chromium; Cybernetics; Genetic algorithms; Guidelines; Humans; Input variables; Performance evaluation; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
  • Conference_Location
    La Jolla, CA
  • Print_ISBN
    0-7803-6375-2
  • Type

    conf

  • DOI
    10.1109/CEC.2000.870794
  • Filename
    870794