• DocumentCode
    299175
  • Title

    Convergence suppression and divergence facilitation: new approach to prune hidden layer and weights of feedforward neural networks

  • Author

    Yasui, Syozo ; Malinowski, Aleksander ; Zurada, Jacek M.

  • Author_Institution
    Neurosystems Lab., Kyushu Inst. of Technol., Fukuoka, Japan
  • Volume
    1
  • fYear
    1995
  • fDate
    30 Apr-3 May 1995
  • Firstpage
    121
  • Abstract
    A pruning algorithm is devised for multilayer multi-output feedforward perceptron networks. The algorithm efficiently reduces the total number of hidden units and the number of weights in the output layer. Test examples include network pruning for the IRIS classifier and for the bitmap digit classifier
  • Keywords
    backpropagation; convergence; feedforward neural nets; multilayer perceptrons; pattern classification; IRIS classifier; bitmap digit classifier; convergence suppression; divergence facilitation; feedforward neural networks; hidden layer; multilayer multi-output feedforward perceptron networks; pruning algorithm; weights; Convergence; Network topology; Neural networks; Neurons; Wiring;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-2570-2
  • Type

    conf

  • DOI
    10.1109/ISCAS.1995.521466
  • Filename
    521466
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=299175