New Exponential Stability Criteria for Neural Networks With Time-Varying Delay

被引：19
|
作者：
Hua, Chang-Chun [1 ,2 ]
Yang, Xian [1 ]
Yan, Jing [1 ]
Guan, Xin-Ping [1 ]
机构：
[1] Yanshan Univ, Inst Elect Engn, Qinhuangdao 066004, Peoples R China
[2] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2011年 / 58卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Exponential stability; linear matrix inequalities (LMIs); Lyapunov method; time-delay neural network; DISCRETE;
D O I：
10.1109/TCSII.2011.2172523
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 is concerned with the global exponential stability analysis problem for neural networks with time-varying delay. We construct an augmented Lyapunov-Krasovskii functional by using the decompositions of the time delays. In order to deal with the integral terms, the different integral intervals with the same interval length are unified. As a result, no extra inequalities are involved. The novel delay-dependent exponential stability criterion is proposed. Numerical examples are given to demonstrate the effectiveness of the obtained results.
引用
收藏
页码：931 / 935
页数：5
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