• DocumentCode
    794487
  • Title

    Alleviating `overfitting´ via genetically-regularised neural network

  • Author

    Chan, Z.S.H. ; Ngan, H.W. ; Rad, A.B. ; Ho, T.K.

  • Author_Institution
    Dept. Of Electr. Eng., Hong Kong Polytech. Univ., Kowloon, China
  • Volume
    38
  • Issue
    15
  • fYear
    2002
  • fDate
    7/18/2002 12:00:00 AM
  • Firstpage
    809
  • Lastpage
    810
  • Abstract
    A hybrid genetic algorithm/scaled conjugate gradient regularisation method is designed to alleviate ANN `over-fitting´. In application to day-ahead load forecasting, the proposed algorithm performs better than early-stopping and Bayesian regularisation, showing promising initial results
  • Keywords
    conjugate gradient methods; generalisation (artificial intelligence); genetic algorithms; learning (artificial intelligence); load forecasting; neural nets; day-ahead load forecasting; genetically-regularised neural network; hybrid genetic algorithm/scaled conjugate gradient regularisation method; overfitting;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:20020592
  • Filename
    1021857
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=794487