• DocumentCode
    1349400
  • Title

    A sufficient condition for absolute stability of a larger class of dynamical neural networks

  • Author

    Arik, Sabri ; Tavsanoglu, Vedat

  • Author_Institution
    Dept. of Electron., Istanbul Univ., Turkey
  • Volume
    47
  • Issue
    5
  • fYear
    2000
  • fDate
    5/1/2000 12:00:00 AM
  • Firstpage
    758
  • Lastpage
    760
  • Abstract
    In this paper, we present a sufficient condition for absolute stability of a larger class of dynamical neural networks. It is shown that the H-matrix condition on the interconnection matrix ensures the existence, uniqueness and global asymptotic stability (GAS) of the equilibrium point with respect to slope-limited activation functions
  • Keywords
    absolute stability; asymptotic stability; matrix algebra; neural nets; transfer functions; H-matrix condition; absolute stability; dynamical neural networks; equilibrium point; global asymptotic stability; interconnection matrix; slope-limited activation functions; sufficient condition; Asymptotic stability; Circuit stability; Design optimization; Matrix converters; Neural networks; Neurons; Nonlinear dynamical systems; Quadratic programming; Stability analysis; Sufficient conditions;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7122
  • Type

    jour

  • DOI
    10.1109/81.847881
  • Filename
    847881
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1349400