• DocumentCode
    1559048
  • Title

    Evolving mixture of experts for nonlinear time series modelling and prediction

  • Author

    Sun-Gi Hong ; Sang-Keon Oh ; Min-Soeng Kim ; Ju-Jang Lee

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea
  • Volume
    38
  • Issue
    1
  • fYear
    2002
  • Firstpage
    34
  • Lastpage
    35
  • Abstract
    The evolutionary structure optimisation (ESO) method for Gaussian radial basis function (RBF) networks has already been presented by the authors. Here, they improve the ESO method in its mutation operator and apply it to a mixture of experts (ME) for modelling and predicting nonlinear time series. The ME implementation provides much better generalisation performance with fewer network parameters, compared to the Gaussian RBF networks.
  • Keywords
    evolutionary computation; generalisation (artificial intelligence); prediction theory; radial basis function networks; time series; ESO method; Gaussian radial basis function networks; evolutionary structure optimisation; generalisation performance; mixture of experts; mutation operator; network parameters; nonlinear prediction; nonlinear time series modelling;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:20020010
  • Filename
    977544
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1559048