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

详细信息    查看全文

• 作者：Jinhui Zhang ; Peng Shi ; Jiqing Qiu ; Hongjiu Yang
• 关键词：Stochastic neural networks ; Time delays ; Exponential stability ; Linear matrix inequalities (LMIs) ; Norm-bounded uncertainties
• 刊名：Mathematical and Computer Modelling
• 出版年：2008
• 出版时间：May 2008
• 年：2008
• 卷：47
• 期：9-10
• 页码：1042-1051
• 全文大小：433 K

文摘

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.