Global exponential stability of a general class of recurrent neural networks with time-varying delays

被引：249
|
作者：
Zeng, ZG [1 ]
Wang, J
Liao, XX
机构：
[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
[2] Hubei Normal Univ, Dept Math, Huangshi 435002, Peoples R China
[3] Chinese Univ Hong Kong, Dept Automat & Comp Aided Engn, Shatin, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2003年 / 50卷 / 10期
关键词：
global exponential stability; neural networks; rate of exponential convergence; time-varying delays;
D O I：
10.1109/TCSI.2003.817760
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 presents new theoretical results on the global exponential stability of neural networks with time-varying delays and Lipschitz continuous activation functions. These results include several sufficient conditions for the global exponential stability of general neural networks with time-varying delays and without monotone, bounded, or continuously differentiable activation function. In addition to providing new criteria for neural networks with time-varying delays, these stability conditions also improve upon the existing ones with constant time delays and without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the results.
引用
收藏
页码：1353 / 1358
页数：6
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