• DocumentCode
    1643803
  • Title

    Convergence of reciprocal time-discrete cellular neural networks with continuous nonlinearities

  • Author

    Fruehau, N. ; Chua, L.O. ; Lueder, E.

  • Author_Institution
    Inst. fuer Netzwerk und Systemtheorie, Stuttgart Univ., Germany
  • fYear
    1992
  • Firstpage
    106
  • Lastpage
    111
  • Abstract
    A proof for the convergence of reciprocal time-discrete cellular neural networks (CNNs) with continuous, monotone increasing nonlinearities is presented. The proof uses a Lyapunov function of the time-discrete cellular neural network
  • Keywords
    Lyapunov methods; convergence; neural nets; Lyapunov function; continuous nonlinearities; convergence; reciprocal time-discrete cellular neural networks; Cellular neural networks; Computer networks; Convergence; Lyapunov method; Nonlinear equations; Output feedback; Piecewise linear techniques; Stability analysis; State feedback;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and their Applications, 1992. CNNA-92 Proceedings., Second International Workshop on
  • Conference_Location
    Munich
  • Print_ISBN
    0-7803-0875-1
  • Type

    conf

  • DOI
    10.1109/CNNA.1992.274346
  • Filename
    274346
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1643803