• DocumentCode
    1323344
  • Title

    Hopfield learning rule with high capacity storage of time-correlated patterns

  • Author

    Storkey, Amos J. ; Valabregue, R.

  • Author_Institution
    Neutral Syst. Group, Imperial Coll. of Sci., Technol. & Med., London, UK
  • Volume
    33
  • Issue
    21
  • fYear
    1997
  • fDate
    10/9/1997 12:00:00 AM
  • Firstpage
    1803
  • Lastpage
    1804
  • Abstract
    A new local and incremental learning rule is examined for its ability to store patterns from a time series in an attractor neural network. This learning rule has a higher capacity than the Hebb rule, and suffers significantly less capacity loss as the correlation between patterns increases
  • Keywords
    Hopfield neural nets; content-addressable storage; learning (artificial intelligence); time series; Hopfield learning rule; attractor neural network; high capacity storage; local and incremental learning rule; time series; time-correlated patterns;
  • fLanguage
    English
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el:19971233
  • Filename
    633404
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1323344