• DocumentCode
    2247087
  • Title

    Research on nonlinear system identification based on input linearization dynamic recurrent neural network

  • Author

    Du, Yun ; Sun, Hui-qin ; Meng, Fan-hua ; Zhang, Su-Ying ; Tian, Qiang

  • Author_Institution
    Hebei Univ. of Sci. & Technol., Shijiazhuang, China
  • Volume
    5
  • fYear
    2010
  • fDate
    11-14 July 2010
  • Firstpage
    2288
  • Lastpage
    2291
  • Abstract
    In this paper, it studies the problems of the on-line identification on the nonlinear and time-lag SISO dynamic system. It puts forward the recurrent structure to linearize the input neurons of the neural network which can describe the feasibility of the algorithm, so the neural network has the dynamic on-line identification capability. Simulation results show that the input linearization dynamic recurrent network has a strong self-adaptability and robustness. It gives a new method for SISO nonlinear dynamic system identification.
  • Keywords
    MIMO systems; identification; linear systems; nonlinear systems; recurrent neural nets; SISO nonlinear dynamic system identification; input linearization dynamic recurrent neural network; nonlinear system identification; online identification; time-lag SISO dynamic system; Artificial neural networks; Equations; Mathematical model; Neurons; Nonlinear dynamical systems; Training; Dynamic recurrent; Input linearization; Neural network; On-line identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4244-6526-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2010.5580651
  • Filename
    5580651