• DocumentCode
    3850268
  • Title

    Gamma-filter self-organising neural networks for unsupervised sequence processing

  • Author

    P.A. Estevez;R. Hernandez;C.A. Perez;C.M. Held

  • Author_Institution
    Department of Electrical Engineering and Advanced Mining Technology Center, Universidad de Chile
  • Volume
    47
  • Issue
    8
  • fYear
    2011
  • fDate
    4/14/2011 12:00:00 AM
  • Firstpage
    494
  • Lastpage
    496
  • Abstract
    Adding γ-filters to self-organising neural networks for unsupervised sequence processing is proposed. The proposed γ-context model is applied to self-organising maps and neural gas networks. The γ-context model is a generalisation that includes as a particular example the previously published merge-context model. The results show that the γ-context model outperforms the merge-context model in terms of temporal quantisation error and state-space representation.
  • Journal_Title
    Electronics Letters
  • Publisher
    iet
  • ISSN
    0013-5194
  • Type

    jour

  • DOI
    10.1049/el.2011.0115
  • Filename
    5751790