• DocumentCode
    1089888
  • Title

    Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution"

  • Author

    Kremer, S.C.

  • Author_Institution
    Communication Res. Centre, Ottawa, Ont., Canada
  • Volume
    7
  • Issue
    4
  • fYear
    1996
  • fDate
    7/1/1996 12:00:00 AM
  • Firstpage
    1047
  • Lastpage
    1051
  • Abstract
    Giles et al. (1995) have proven that Fahlman´s recurrent cascade correlation (RCC) architecture is not capable of realizing finite state automata that have state-cycles of length more than two under a constant input signal. This paper extends the conclusions of Giles et al. by showing that there exists a corollary to their original proof which identifies a large second class of automata, that is also unrepresentable by RCC.
  • Keywords
    automata theory; learning (artificial intelligence); recurrent neural nets; constructive learning; finite state automata; recurrent cascade correlation; recurrent neural networks; state-cycles; Labeling; Learning automata; Neural networks; Recurrent neural networks; Sun;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.508949
  • Filename
    508949