• DocumentCode
    2292754
  • Title

    Multiperiodicity and attractivity analysis for a class of high-order Cohen-Grossberg neural networks

  • Author

    Sheng, Li ; Gao, Ming

  • Author_Institution
    Coll. of Inf. & Control Eng., China Univ. of Pet. (East China), Qingdao, China
  • fYear
    2012
  • fDate
    6-8 July 2012
  • Firstpage
    1489
  • Lastpage
    1494
  • Abstract
    In this paper, the multiperiodicity of a class of high-order Cohen-Grossberg neural networks (HOCGNNs) with special activation functions is discussed by using analysis approach and decomposition of state space. The activation functions of this class of neural networks consist of nondecreasing functions with saturation, standard activation functions of cellular neural networks, etc. It is shown that the n-neuron HOCGNNs can have 2n locally exponentially attractive periodic orbits located in saturation regions. In addition, a condition is derived for ascertaining the periodic orbit to be locally exponentially attractive and to be located in any designated region. Finally, an example is given to show the effectiveness of the obtained results.
  • Keywords
    cellular neural nets; state-space methods; transfer functions; attractivity analysis; cellular neural networks; high-order Cohen-Grossberg neural networks; multiperiodicity analysis; n-neuron HOCGNN; periodic orbit; saturation regions; standard activation functions; state space decomposition; Biological neural networks; Educational institutions; Limit-cycles; Orbits; Space vehicles; Vectors; Exponentially attractive; High-order Cohen-Grossberg neural networks; Multiperiodicity; Multistability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2012 10th World Congress on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-1397-1
  • Type

    conf

  • DOI
    10.1109/WCICA.2012.6358114
  • Filename
    6358114