• DocumentCode
    393467
  • Title

    Auto correlation associative memory by Universal Learning Networks (ULNs)

  • Author

    Shibuta, Keiko ; Hirasawa, Kotaro ; Hu, Jinglu ; Murata, Junichi

  • Author_Institution
    Graduate Sch. of Inf. Sci & Electr. Eng.., Kyushu Univ., Japan
  • Volume
    2
  • fYear
    2002
  • fDate
    5-7 Aug. 2002
  • Firstpage
    753
  • Abstract
    In this paper, we propose a new auto correlation associative memory using Universal Learning Networks (ULNs). It enables not only to obtain associative memory by optimizing parameters but also to store more memories than conventional models by introducing "don\´t care nodes" or "sensitivity term". This is expected to settle some problems related to associative memory.
  • Keywords
    content-addressable storage; learning (artificial intelligence); neural nets; recurrent neural nets; RasID; ULNs; Universal Learning Networks; associative memory; auto correlation associative memory; Associative memory; Autocorrelation; Biological neural networks; Brain modeling; Computer simulation; Control system synthesis; Delay effects; Gold; Neural networks; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE 2002. Proceedings of the 41st SICE Annual Conference
  • Print_ISBN
    0-7803-7631-5
  • Type

    conf

  • DOI
    10.1109/SICE.2002.1195251
  • Filename
    1195251
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=393467