• DocumentCode
    1264450
  • Title

    Sufficient condition for convergence of a relaxation algorithm in actual single-layer neural networks

  • Author

    Zurada, Jacek M. ; Shen, Weigong

  • Author_Institution
    Dept. of Electr. Eng., Louisville Univ., KY, USA
  • Volume
    1
  • Issue
    4
  • fYear
    1990
  • fDate
    12/1/1990 12:00:00 AM
  • Firstpage
    300
  • Lastpage
    303
  • Abstract
    Application of the contraction mapping theorem to single-layer feedback neural networks of a gradient-type is discussed. The sufficient condition for stability of a relaxation algorithm in actual continuous-time networks is derived and illustrated with an example. Results showing the stability of a numerical solution obtained with the relaxation algorithm are presented
  • Keywords
    convergence of numerical methods; neural nets; relaxation theory; stability; continuous-time networks; contraction mapping theorem; convergence; relaxation algorithm; single-layer neural networks; stability; sufficient condition; Character recognition; Convergence; Equations; Gaussian noise; Intelligent networks; Multilayer perceptrons; Nearest neighbor searches; Neural networks; Sonar detection; Sufficient conditions;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.80268
  • Filename
    80268
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1264450