• DocumentCode
    2398085
  • Title

    SIMO Fourier neural networks research

  • Author

    Yang, Xuhua ; Dai, Huaping ; Sun, Yowian

  • Author_Institution
    Inst. of Modern Control Eng., Zhejiang Univ., Hangzhou, China
  • Volume
    2
  • fYear
    2003
  • fDate
    12-15 Oct. 2003
  • Firstpage
    1606
  • Abstract
    This paper proposed the single input multiple outputs (SIMO) Fourier neural networks on the base of Fourier series principle. The SIMO Fourier neural networks turn nonlinear optimization problem into linear optimization problem. So, the SIMO Fourier neural networks highly improve convergence speed and avoid local minima problem. At the same time, under the condition of bounded input and bounded output, the SIMO Fourier neural networks can approximate multiple arbitrary nonlinear mapping relationship at arbitrary accuracy and have good generalization capability.
  • Keywords
    Fourier series; backpropagation; neural nets; optimisation; Fourier series principle; convergence speed; linear optimization; local minima problem; nonlinear mapping relationship; nonlinear optimization problem; single input multiple outputs Fourier neural networks; Control engineering; Convergence; Fourier series; Industrial control; Laboratories; Modems; Neural networks; Optimization methods; Random processes; Sun;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE
  • Print_ISBN
    0-7803-8125-4
  • Type

    conf

  • DOI
    10.1109/ITSC.2003.1252755
  • Filename
    1252755