Delay-dependent exponential stability of neural networks with variable delay: An LMI approach

被引：72

作者：

Chen, Wu-Hua ^{[1
]}

Lu, Xiaomei

Guan, Zhi-Hong

Zheng, Wed Xing

机构：

[1] Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Peoples R China

[2] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China

[3] Univ Western Sydney, Sch Comp & Math, Penrith, NSW 1797, Australia

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2006年 / 53卷 / 09期

关键词：

delay-dependent criteria; exponential stability; linear matrix inequality (LMI); neural networks; variable delay;

D O I：

10.1109/TCSII.2006.881824

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This brief focuses on the problem of delay-dependent stability analysis of neural networks with variable delay. Two types of variable delay are considered: one is differentiable and has bounded derivative; the other one is continuous and may vary very fast. By introducing a new type of Lyapunov-Krasovskii functional, new delay-dependent sufficient conditions for exponential stability of delayed neural networks are derived in terms of linear matrix inequalities. We also obtain delay-independent stability criteria. Two examples are presented which show our results are less conservative than the existing stability criteria.

引用

页码：837 / 842

页数：6

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