• DocumentCode
    1242494
  • Title

    Recursive single-layer nets for output error dynamic models

  • Author

    Berger, C.S.

  • Author_Institution
    Dept. of Electr. & Comput. Syst. Eng., Morash Univ., Clayton, Vic., Australia
  • Volume
    6
  • Issue
    2
  • fYear
    1995
  • fDate
    3/1/1995 12:00:00 AM
  • Firstpage
    508
  • Lastpage
    511
  • Abstract
    An algorithm for training recursive single-layer nets that has been shown to exhibit rapid convergence is presented. Convergence is not guaranteed, but a sufficient condition is given to justify the method. The method is demonstrated on a difficult modeling problem from bioengineering
  • Keywords
    learning (artificial intelligence); neural nets; bioengineering; output error dynamic models; rapid convergence; recursive single-layer nets; sufficient condition; Biomedical engineering; Convergence; Cost function; Equations; Feedforward neural networks; Iterative algorithms; Least squares approximation; Neural networks; Predictive models; Sufficient conditions;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.363491
  • Filename
    363491
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1242494