• DocumentCode
    3402788
  • Title

    Basis vector analyses of back-propagation neural networks

  • Author

    Chen, Mu-Song ; Manry, Michael T.

  • Author_Institution
    Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA
  • fYear
    1991
  • fDate
    14-17 May 1991
  • Firstpage
    23
  • Abstract
    Develops a polynomial basis function approach for modeling BP (backpropagation) neural networks. This method leads directly to a constructive proof of the BP approximation theorem. In addition, the basis vector approach provides a means to synthesize the BP neural network output as a polynomial function. An algorithm for pruning the useless basis vectors is also demonstrated
  • Keywords
    backpropagation; neural nets; polynomials; BP approximation theorem; back-propagation neural networks; basis vector approach; constructive proof; polynomial function; Convergence; Filtering; Joining processes; Network topology; Neural networks; Polynomials; Taylor series;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1991., Proceedings of the 34th Midwest Symposium on
  • Conference_Location
    Monterey, CA
  • Print_ISBN
    0-7803-0620-1
  • Type

    conf

  • DOI
    10.1109/MWSCAS.1991.252222
  • Filename
    252222
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3402788