• DocumentCode
    2243852
  • Title

    Chaos prediction and inverse system control based on fuzzy-neural network

  • Author

    Haipeng, Ren ; Ding, Liu

  • Author_Institution
    Xi´´an Univ. of Technol., China
  • Volume
    4
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    148
  • Abstract
    The Sugeno fuzzy-neuron network (FNN) is employed to establish the inverse system model of chaotic systems, and the inverse system method is used to control chaos. The characteristics of this method is learning the motion principles of the chaotic system by FNN and controlling chaos effectively with learned principles instead of establishing the exact analytical model of the chaotic system. Moreover, this method does not require the control objective to be a stationary point or a periodic trajectory. Theoretical analysis and simulations with Logistic and Henon mappings prove that this method is effective
  • Keywords
    Henon mapping; chaos; discrete systems; fuzzy neural nets; learning (artificial intelligence); neurocontrollers; nonlinear dynamical systems; Henon mapping; Logistic mapping; Sugeno fuzzy neuron network; chaos prediction; fuzzy-neural network; inverse system control; inverse system model; motion principles; Analytical models; Biological control systems; Chaos; Control system synthesis; Control systems; Inverse problems; Motion analysis; Nonlinear dynamical systems; Postal services; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Info-tech and Info-net, 2001. Proceedings. ICII 2001 - Beijing. 2001 International Conferences on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-7010-4
  • Type

    conf

  • DOI
    10.1109/ICII.2001.983797
  • Filename
    983797