• DocumentCode
    3543053
  • Title

    Advantage analysis of sigmoid based RBF networks

  • Author

    Xing Wu ; Wilamowski, Bogdan M.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Auburn Univ., Auburn, AL, USA
  • fYear
    2013
  • fDate
    19-21 June 2013
  • Firstpage
    243
  • Lastpage
    248
  • Abstract
    By introducing an extra dimension to the inputs, sigmoid function can simulate the behavior of traditional RBF units. This paper introduces a sigmoid based RBF neuron and compares it with traditional RBF neuron. Neural networks composed of these neurons are trained with ErrCor algorithm on two classic experiments. Comparison results are presented to show advantages of the sigmoid based RBF model.
  • Keywords
    radial basis function networks; ErrCor algorithm; advantage analysis; neural networks; sigmoid based RBF networks; sigmoid based RBF neuron; Algorithm design and analysis; Approximation algorithms; Biological neural networks; Function approximation; Neurons; Radial basis function networks; Training; ErrCor algorithm; RBF; neural network; sigmoid;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Engineering Systems (INES), 2013 IEEE 17th International Conference on
  • Conference_Location
    San Jose
  • Print_ISBN
    978-1-4799-0828-8
  • Type

    conf

  • DOI
    10.1109/INES.2013.6632819
  • Filename
    6632819
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3543053