• DocumentCode
    523649
  • Title

    Nonlinear System Modeling Based on IFCNN

  • Author

    Xia, Liu ; Yubo, Duan ; Xiuju, Yang

  • Author_Institution
    Dept. of Electr. & Inf. Eng., Daqing Pet. Inst., Daqing, China
  • Volume
    2
  • fYear
    2010
  • fDate
    11-12 May 2010
  • Firstpage
    813
  • Lastpage
    816
  • Abstract
    This paper for the shortcomings of conventional BP algorithm which has slow convergence and falls into local minimum easily, the nonlinear self-feedback term is introduced into this algorithm. Thus chaotic BP algorithm (CBPA) is given. The weight of fuzzy neural network (FNN) is trained and learned by using it. Thus an introduction-type fuzzy chaotic neural network (IFCNN) is constituted. Then simulation of nonlinear system based on IFCNN given is proposed. Simulation results show that the designed IFCNN has the same and complex dynamic characteristics with chaotic system, which has good modeling capabilities for nonlinear system. And with the chaotic BP algorithm training parameters, it has fast convergence, mixed search capability, being able to be out of local minimum.
  • Keywords
    backpropagation; chaos; feedback; fuzzy neural nets; modelling; nonlinear control systems; IFCNN; backpropagation; chaotic BP algorithm; complex dynamic characteristics; conventional BP algorithm; fuzzy chaotic neural network; mixed search capability; nonlinear system modeling; Approximation algorithms; Automation; Chaos; Convergence; Fuzzy neural networks; Interference; Modeling; Neural networks; Nonlinear systems; Petroleum; Chaotic BP Algorithm; Fuzzy Neural Network; Nonlinear System Modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4244-7279-6
  • Electronic_ISBN
    978-1-4244-7280-2
  • Type

    conf

  • DOI
    10.1109/ICICTA.2010.421
  • Filename
    5522759