• DocumentCode
    1265011
  • Title

    Input-to-state stability (ISS) analysis for dynamic neural networks

  • Author

    Sanchez, Edgar N. ; Perez, Jose P.

  • Author_Institution
    Sch. of Phys. & Math. Sci., Univ. Autonoma de Nuevo Leon, Mexico
  • Volume
    46
  • Issue
    11
  • fYear
    1999
  • fDate
    11/1/1999 12:00:00 AM
  • Firstpage
    1395
  • Lastpage
    1398
  • Abstract
    In this paper a novel approach to assess the stability of dynamic neural networks is presented. Using a Lyapunov function, we determine conditions to guarantee input-to-state stability (ISS) which also ensures global asymptotic stability (GAS). The applicability of these conditions is illustrated by two examples
  • Keywords
    Lyapunov methods; asymptotic stability; neural nets; stability; Lyapunov function; dynamic neural networks; global asymptotic stability; input-to-state stability analysis; Associative memory; Asymptotic stability; Automatic control; Lyapunov method; Mathematical model; Neural networks; Nonlinear systems; Pattern recognition; Physics; Stability analysis;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7122
  • Type

    jour

  • DOI
    10.1109/81.802844
  • Filename
    802844
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1265011