• DocumentCode
    2466670
  • Title

    Genetic Programming Based Multichannel Identification of Nonlinear Systems by Volterra Filters

  • Author

    Yao, Leehter ; Lin, Chin-Chin

  • Author_Institution
    Nat. Taipei Univ. of Technol., Taipei
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    2864
  • Lastpage
    2871
  • Abstract
    Genetic programming (GP) is utilized to search the optimal structure of Volterra filter in this paper. The Volterra filter with high order and large memories contains great amount of cross product terms. Instead of applying GP to search all cross products, GP is utilized to search a smaller set of primary signals which evolve to the whole set of cross products. With GP´s optimization capability, the important primary signals and the associated cross products of input signals attributing most to the outputs will be chosen while the primary signals and their associated cross products of input signals which are trivial to the outputs will be excluded from the possible candidate primary signals.
  • Keywords
    genetic algorithms; nonlinear filters; nonlinear systems; Volterra filter; genetic programming; multichannel identification; nonlinear system; optimization capability; primary signals; Delay; Filter bank; Genetic programming; Kernel; Least squares approximation; Linear regression; Nonlinear filters; Nonlinear systems; Resonance light scattering; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9487-9
  • Type

    conf

  • DOI
    10.1109/CEC.2006.1688669
  • Filename
    1688669