Robust exponential stability for uncertain stochastic neural networks with discrete and distributed time-varying delays

Hongyi Li, B. Chen, Q. Zhou, S. Fang

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Abstract

This letter deals with the problem of delay-dependent robust exponential stability in mean square for a class of uncertain stochastic Hopfield neural networks with discrete and distributed time-varying delays. Based on Lyapunov–Krasovskii functional and the stochastic stability theory, delay-dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Because of introducing some free-weighting matrices to develop the stability criteria, the proposed stability conditions have less conservatism. Numerical examples are given to illustrate the effectiveness of our results.
Original languageEnglish
Pages (from-to)3385-3394
Number of pages10
JournalPhysics Letters A
Volume372
Issue number19
DOIs
Publication statusPublished - 5 May 2008

Keywords

  • Neural networks
  • Exponential stability
  • Stochastic systems
  • Uncertain systems
  • LMIs

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