• DocumentCode
    3547466
  • Title

    Complex dynamics in a class of nearly-symmetric competitive CNNs

  • Author

    Di Marco, M. ; Forti, M. ; Grazzini, M. ; Pancioni, L.

  • Author_Institution
    Dipt. di Ingegneria dell´´Informazione, Siena Univ., Italy
  • fYear
    2005
  • fDate
    23-26 May 2005
  • Firstpage
    4677
  • Abstract
    The paper analyzes bifurcations and complex dynamics in a class of nearly symmetric standard cellular neural networks (CNN). A one-parameter family of fourth-order CNN is introduced, which exhibits a cascade of period-doubling bifurcations leading to the birth of a complex attractor, close to some nominal symmetric CNN. The novelty with respect to previous work on this topic, is that the bifurcations and complex dynamics are obtained for small relative errors with respect to the nominal interconnections. The dynamical properties of the introduced class of fourth-order CNN, which are characterized by negative (inhibitory) interconnections between distinct neurons, are explained on the basis of a technique proposed by Smale (1976) to embed a given dynamical system within a competitive dynamical system of larger order.
  • Keywords
    bifurcation; cellular neural nets; stability; unsupervised learning; cellular neural networks; competitive dynamical system; complex attractor; complex dynamics; distinct neurons; fourth-order CNN; nearly symmetric competitive CNN; negative inhibitory interconnections; one-parameter family; period-doubling bifurcations; Bifurcation; Cellular neural networks; Computer simulation; Displays; Electronic mail; Frequency domain analysis; Neurons; Robust stability; Symmetric matrices; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on
  • Print_ISBN
    0-7803-8834-8
  • Type

    conf

  • DOI
    10.1109/ISCAS.2005.1465676
  • Filename
    1465676