• DocumentCode
    2285064
  • Title

    Almost sure exponential stability of stochastic recurrent neural networks with time-varying delays

  • Author

    Wan, Li ; Zhou, Qinghua

  • Author_Institution
    Coll. of Sci., Wuhan Textile Univ., Wuhan, China
  • Volume
    2
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    1066
  • Lastpage
    1069
  • Abstract
    The stability of stochastic recurrent neural networks with time-varying delays is investigated. When the delay functions are not differentiable or the information on their derivation is unknown, the sufficient criteria on almost sure exponential stability have been established. One example is also given to illustrate the effectiveness of our results.
  • Keywords
    asymptotic stability; delays; recurrent neural nets; stability criteria; stochastic processes; delay function; exponential stability; stochastic recurrent neural network; time-varying delay; Artificial neural networks; Asymptotic stability; Delay; Recurrent neural networks; Stability criteria; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5582991
  • Filename
    5582991
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2285064