• DocumentCode
    2313811
  • Title

    The normalized radial basis function neural network

  • Author

    Heimes, Felix ; Van Heuveln, Bram

  • Author_Institution
    Lockheed Martin Control Syst., Johnson City, NY, USA
  • Volume
    2
  • fYear
    1998
  • fDate
    11-14 Oct 1998
  • Firstpage
    1609
  • Abstract
    Presents a neural network called the normalized radial basis function (NRBF) neural network. The NRBF integrates techniques from two similar neural networks: the general regression neural network (GRNN) and the radial basis function (RBF) neural network. The NRBF is identical to the standard radial basis function (RBF) network except the hidden layer outputs are normalized before being passed through the output layer. The normalization of the hidden layer weights is shown to improve the extrapolation performance of the conventional RBF network. We have reason to believe that under normal circumstances the NRBF outperforms the RBF and the GRNN
  • Keywords
    extrapolation; radial basis function networks; extrapolation performance; general regression neural network; hidden layer outputs; normalized radial basis function neural network; Cities and towns; Clustering algorithms; Control systems; Extrapolation; Least squares approximation; Neural networks; Radial basis function networks; Stock markets; Training data; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
  • Conference_Location
    San Diego, CA
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-4778-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.1998.728118
  • Filename
    728118