• DocumentCode
    1743950
  • Title

    Cellular neural networks considering hysteresis characteristic

  • Author

    Namba, M. ; Kawabata, H. ; Kanagawa, A. ; Zhang, Z.

  • Author_Institution
    Fac. of Comput. Sci. & Syst. Eng., Okayama Prefectural Univ., Japan
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    352
  • Lastpage
    355
  • Abstract
    The Cellular Neural Network (CNN) has been widely used for associative memory, but has a problem called indeterminate cell. In this paper, we have proposed a CNN considering hysteresis characteristic as one of the methods to avoid the indeterminate cell problem, and confirmed its effectiveness in simulations
  • Keywords
    cellular neural nets; content-addressable storage; hysteresis; CNN; associative memory; cellular neural networks; hysteresis characteristic; indeterminate cell problem; Analog circuits; Associative memory; Cellular neural networks; Circuit simulation; Computer industry; Computer science; Differential equations; Hysteresis; Programmable control; Systems engineering and theory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    0-7803-6253-5
  • Type

    conf

  • DOI
    10.1109/APCCAS.2000.913507
  • Filename
    913507