• DocumentCode
    489383
  • Title

    Radial Basis Function Networks Applied to Process Control

  • Author

    Hofland, A.G. ; Morris, A.J. ; Montague, G.A.

  • Author_Institution
    Department of Chemical and Process Engineering, University of Newcastle, Newcastle-upon-Tyne, NE1 7RU, U.K.
  • fYear
    1992
  • fDate
    24-26 June 1992
  • Firstpage
    480
  • Lastpage
    484
  • Abstract
    There are strong relationships between radial basis function (RBF) approaches and neural network representations. Indeed, the RBF representation can be implementated in the form of a two-layered network. This paper examines the contribution that RBF networks can make to the process modelling and control toolbox. Radial basis function networks are compared with sigmoidal activation function feedforward networks using data from a large industrial process.
  • Keywords
    Approximation methods; Artificial neural networks; Content addressable storage; Network topology; Neurons; Parameter estimation; Process control; Radial basis function networks; System identification; Tellurium;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 1992
  • Conference_Location
    Chicago, IL, USA
  • Print_ISBN
    0-7803-0210-9
  • Type

    conf

  • Filename
    4792112
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=489383