• DocumentCode
    2346259
  • Title

    Wavelet networks for functional learning

  • Author

    Zhang, Jun ; Walter, GiEbert G.

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA
  • fYear
    1994
  • fDate
    27-29 Oct 1994
  • Firstpage
    104
  • Abstract
    A wavelet-based neural network is described. The network is similar to the radial basis function (RBF) network, except that the RBF´s are replaced by orthonormal scaling functions. It has been shown that the wavelet network has universal and L2 approximation properties and is a consistent function estimator. Convergence rates, which avoid the “curse of dimensionality,” are obtained for certain function classes. The network also compared favorably to the MLP and RBF networks in the experiments
  • Keywords
    convergence; feedforward neural nets; learning (artificial intelligence); wavelet transforms; L2 approximation properties; consistent function estimator; convergence rates; functional learning; neural network; orthonormal scaling functions; radial basis function network; universal properties; wavelet networks; Convergence; Mathematics; Mean square error methods; Mercury (metals); Multilayer perceptrons; Neural networks; Radial basis function networks; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
  • Conference_Location
    Alexandria, VA
  • Print_ISBN
    0-7803-2761-6
  • Type

    conf

  • DOI
    10.1109/WITS.1994.513931
  • Filename
    513931
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2346259