• DocumentCode
    1423681
  • Title

    Magnified gradient function in adaptive learning: the MGFPROP algorithm

  • Author

    Ng, Sin-Chun ; Cheung, Chi-Chung ; Leung, Shu-hung

  • Author_Institution
    Dept. of Comput. & Math., Inst. of Vocational Educ., Hong Kong, China
  • Volume
    37
  • Issue
    1
  • fYear
    2001
  • fDate
    1/4/2001 12:00:00 AM
  • Firstpage
    42
  • Lastpage
    43
  • Abstract
    A new algorithm is proposed to solve the “flat spot” problem in backpropagation neural networks by magnifying the gradient function. Simulation results show that, in terms of the convergence rate and the percentage of global convergence, the new algorithm consistently outperforms other traditional methods
  • Keywords
    backpropagation; convergence; feedforward neural nets; MGFPROP algorithm; adaptive learning; backpropagation neural networks; convergence rate; flat spot problem solution; global convergence; magnified gradient function;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:20010002
  • Filename
    894354
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1423681