• DocumentCode
    1190526
  • Title

    The Widrow-Hoff algorithm for McCulloch-Pitts type neurons

  • Author

    Hui, Stefen ; Zak, Stanislaw H.

  • Author_Institution
    Dept. of Math. Sci., San Diego State Univ., CA, USA
  • Volume
    5
  • Issue
    6
  • fYear
    1994
  • fDate
    11/1/1994 12:00:00 AM
  • Firstpage
    924
  • Lastpage
    929
  • Abstract
    We analyze the convergence properties of the Widrow-Hoff delta rule applied to McCulloch-Pitts type neurons. We give sufficiency conditions under which the learning parameters converge and conditions under which the learning parameters diverge. In particular, we analyze how the learning rate affects the convergence of the learning parameters
  • Keywords
    adaptive systems; learning (artificial intelligence); neural nets; parallel algorithms; McCulloch-Pitts type neurons; Widrow-Hoff algorithm; Widrow-Hoff delta rule; convergence; learning parameters; learning rate; neural networks; sufficiency conditions; Adaptive algorithm; Algorithm design and analysis; Bridges; Convergence; Error correction; Iterative algorithms; Neurons;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.329689
  • Filename
    329689
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1190526