• DocumentCode
    285264
  • Title

    Backpropagation algorithm in higher order neural network

  • Author

    Chang, Chirho ; Cheung, J.Y.

  • Author_Institution
    Oklahoma Univ., Norman, OK, USA
  • Volume
    3
  • fYear
    1992
  • fDate
    7-11 Jun 1992
  • Firstpage
    511
  • Abstract
    The supervised backpropagation learning scheme is used to develop a training algorithm for multilayer higher-order neural networks (HONNs). By restructuring the basic HONN architecture, the traditional backpropagation algorithm can be extended to multilayer HONNs. The TC pattern recognition problem is used to compare the performances of various HONNs with different numbers of hidden layers, different numbers of processing elements, and different orders. Simulation results show that, in many causes, the HONN with the same number of training iterations worked better than the conventional first-order networks
  • Keywords
    backpropagation; neural nets; pattern recognition; multilayer higher-order neural networks; pattern recognition; supervised backpropagation learning scheme; training iterations; Artificial neural networks; Backpropagation algorithms; Control system synthesis; Image analysis; Image recognition; Intelligent networks; Multi-layer neural network; Neural networks; Pattern recognition; Power system modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1992. IJCNN., International Joint Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-7803-0559-0
  • Type

    conf

  • DOI
    10.1109/IJCNN.1992.227123
  • Filename
    227123