Radial Basis Function Network in Reproducing Kernel Hilbert Space

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Author(s)

Abstract

The present study employs an idea of mapping data into a high dimensional feature space which is known as Reproducing Kernel Hilbert Space (RKHS), then performs Radial Basis Function (RBF) network in the feature space where the new basis function will be obtained and finally, Orthogonal Least Squares (OLS) method is employed to select a suitable set of centers (regressors) from a large set of candidates in order to obtain a sparse regression model in the feature space. The proposed method is employed to the simple scalar function estimation problems and nonlinear system identification problem by simulations.

Journal

  • Research reports on information science and electrical engineering of Kyushu University

    Research reports on information science and electrical engineering of Kyushu University 10(1), 9-14, 2005-03

    Kyushu University

Codes

  • NII Article ID (NAID)
    110001131525
  • NII NACSIS-CAT ID (NCID)
    AN10569524
  • Text Lang
    ENG
  • Article Type
    departmental bulletin paper
  • Journal Type
    大学紀要
  • ISSN
    13423819
  • NDL Article ID
    7693442
  • NDL Source Classification
    ZM2(科学技術--科学技術一般--大学・研究所・学会紀要)
  • NDL Call No.
    Z14-B425
  • Data Source
    NDL  NII-ELS  IR 
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