• DocumentCode
    289399
  • Title

    Comparison of gradient based training algorithms for multilayer perceptrons

  • Author

    Irwin, George ; Lightbody, Gordon ; McLoone, Sean

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK
  • fYear
    1994
  • fDate
    25-27 May 1994
  • Firstpage
    42675
  • Lastpage
    42680
  • Abstract
    The training speed of batch backpropagation using steepest descent, conjugate gradient and quasi-Newton algorithm for a feedforward neural network are compared. Results illustrating the advantages of the Hessian based techniques are given and issues affecting speed discussed
  • Keywords
    Hessian matrices; Newton method; backpropagation; conjugate gradient methods; feedforward neural nets; multilayer perceptrons; Hessian based techniques; batch backpropagation; conjugate gradient; feedforward neural network; gradient based training algorithms; multilayer perceptrons; quasi-Newton algorithm; steepest descent;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Advances in Neural Networks for Control and Systems, IEE Colloquium on
  • Conference_Location
    Berlin
  • Type

    conf

  • Filename
    381761
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=289399