• DocumentCode
    2955597
  • Title

    Kohonen feature map associative memory with area representation for sequential analog patterns

  • Author

    Shiratori, Tomonori ; Osana, Yuko

  • Author_Institution
    Tokyo Univ. of Technol., Tokyo
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    818
  • Lastpage
    823
  • Abstract
    In this paper, we propose a Kohonen feature map associative memory with area representation for sequential analog patterns. This model is based on the Kohonen feature map associative memory with area representation for sequential patterns. Although the conventional Kohonen feature map associative memory with area representation for sequential patterns can deal with only binary (bipolar) patterns, the proposed model can deal not only binary (bipolar) patterns but also analog patterns. The proposed model can learn sequential analog patterns successively, and has robustness for damaged neurons. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed model.
  • Keywords
    content-addressable storage; learning (artificial intelligence); self-organising feature maps; Kohonen feature map associative memory; area representation; sequential analog pattern learning; Associative memory; Hebbian theory; Information processing; Neural networks; Neurons; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4633891
  • Filename
    4633891