A new criterion for exponential stability of uncertain stochastic neural networks with mixed delays

Jinhui Zhang, Peng Shi, Jiqing Qiu*, Hongjiu Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

This paper deals with the problem of exponential stability for a class of uncertain stochastic neural networks with both discrete and distributed delays (also called mixed delays). The system possesses time-varying and norm-bounded uncertainties. Based on Lyapunov-Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities to guarantee the delayed neural networks to be robustly exponentially stable in the mean square for all admissible parameter uncertainties. Numerical examples are given to illustrate the effectiveness of the developed techniques.

Original languageEnglish
Pages (from-to)1042-1051
Number of pages10
JournalMathematical and Computer Modelling
Volume47
Issue number9-10
DOIs
Publication statusPublished - May 2008

Keywords

  • Exponential stability
  • Linear matrix inequalities (LMIs)
  • Norm-bounded uncertainties
  • Stochastic neural networks
  • Time delays

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