• DocumentCode
    1541289
  • Title

    Learning algorithms for perceptions using back-propagation with selective updates

  • Author

    Huang, Shih-Chi ; Huang, Yih-Fang

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Notre Dame Univ., IN, USA
  • Volume
    10
  • Issue
    3
  • fYear
    1990
  • fDate
    4/1/1990 12:00:00 AM
  • Firstpage
    56
  • Lastpage
    61
  • Abstract
    The error back-propagation algorithm for perceptrons is studied, and an extension of this algorithm that features selective learning is introduced. In selective learning, one of two selection criteria is used to screen the input data to improve the convergence property of the back-propagation algorithm. An associative content addressable memory using multilayer perceptrons is devised to demonstrate the improver convergence.<>
  • Keywords
    artificial intelligence; content-addressable storage; learning systems; associative content addressable memory; back-propagation; convergence; learning algorithms; perceptions; Artificial neural networks; Control systems; Convergence; Integrated circuit interconnections; Multilayer perceptrons; Neural networks; Neurons; Nonlinear control systems; Supervised learning; Very large scale integration;
  • fLanguage
    English
  • Journal_Title
    Control Systems Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1708
  • Type

    jour

  • DOI
    10.1109/37.55125
  • Filename
    55125
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1541289