• DocumentCode
    420577
  • Title

    A new fuzzy modeling and identification based on fast-cluster and genetic algorithm

  • Author

    Liu, Fucai ; Lu, Pingli ; Pei, Run

  • Author_Institution
    Dept. of Autom., Yanshan Univ., Qin-Huangdao, China
  • Volume
    1
  • fYear
    2004
  • fDate
    15-19 June 2004
  • Firstpage
    290
  • Abstract
    A new fuzzy identification algorithm is proposed in this paper, which include five blocks: input variables partition block, fast-cluster block, genetic algorithm block, tuning block and termination block. Fast-cluster block is to identify antecedent parameters of T-S model speedily. Tuning block is to fine-tune the parameters of T-S model using the gradient descent approach and termination block checks if the result is satisfactory. The proposed algorithm not only has the advantage of simplicity, but also has high accuracy, strong automation. The simulations indicate that the algorithm is effective in constructing T-S model for complex nonlinear systems.
  • Keywords
    fuzzy set theory; genetic algorithms; gradient methods; identification; modelling; nonlinear control systems; T-S model; complex nonlinear control systems; fast cluster block; fuzzy identification algorithm; fuzzy modeling; genetic algorithm block; gradient descent approach; tuning block; Automation; Control engineering; Fuzzy control; Genetic algorithms; Input variables; Nonlinear systems; Partitioning algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
  • Print_ISBN
    0-7803-8273-0
  • Type

    conf

  • DOI
    10.1109/WCICA.2004.1340576
  • Filename
    1340576