• DocumentCode
    1603843
  • Title

    Stability analysis of nonlinear neural network models

  • Author

    Xiong, Kaiqi

  • Author_Institution
    Dept. of Math., Claremont Graduate Sch., CA, USA
  • Volume
    4
  • fYear
    1996
  • Firstpage
    842
  • Abstract
    The problem of stability for nonlinear neural networks is addressed in this paper. By means of the Lyapunov function of Lurie type, new classes of stability conditions for general neural network models are presented. The stability analysis here is global in the space of neuronal activations. An illustrated example is given
  • Keywords
    Lyapunov methods; asymptotic stability; neural nets; Lurie Lyapunov function; asymptotic stability; global analysis; neural network models; neuronal activations; nonlinear neural network; stability analysis; stability conditions; Asymptotic stability; Cellular neural networks; Hopfield neural networks; Large-scale systems; Lyapunov method; Mathematics; Neural networks; Neurons; Signal processing; Stability analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
  • Conference_Location
    Atlanta, GA
  • Print_ISBN
    0-7803-3073-0
  • Type

    conf

  • DOI
    10.1109/ISCAS.1996.542156
  • Filename
    542156
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1603843