• DocumentCode
    1543513
  • Title

    Maximum certainty approach to feedforward neural networks

  • Author

    Roberts, S.J. ; Penny, W.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
  • Volume
    33
  • Issue
    4
  • fYear
    1997
  • fDate
    2/13/1997 12:00:00 AM
  • Firstpage
    306
  • Lastpage
    307
  • Abstract
    A Bayesian-based methodology is presented which leads to a data analysis system based around a committee of radial-basis function (RBF) networks. The authors show that this approach enables estimation of the uncertainty associated with system outputs. Systems with differing numbers of internal degrees of freedom (weights) may hence be compared using training data only
  • Keywords
    Bayes methods; data analysis; feedforward neural nets; learning (artificial intelligence); Bayesian-based methodology; RBF networks; data analysis system; feedforward neural networks; maximum certainty approach; radial-basis function networks; system output uncertainty; training data; uncertainty estimation; weights;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:19970211
  • Filename
    583491
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1543513