The globally asymptotic stability analysis for a class of recurrent neural networks with delays

被引：0
|
作者：
Xingguo Song
Haibo Gao
Liang Ding
Deyou Liu
Minghui Hao
机构：
[1] Harbin Institute of Technology,State Key Laboratory of Robotics and System
[2] Yanshan University,undefined
来源：
Neural Computing and Applications | 2013年 / 22卷
关键词：
Neural networks (NN); Global asymptotic stability (GAS); Equilibrium point; Delay; Lie algebra;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper considers the problem of global stability of neural networks with delays. By combining Lie algebra and the Lyapunov function with the integral inequality technique, we analyze the globally asymptotic stability of a class of recurrent neural networks with delays and give an estimate of the exponential stability. A few new sufficient conditions and criteria are proposed to ensure globally asymptotic stability of the equilibrium point of the neural networks. A few simulation examples are presented to demonstrate the effectiveness of the results and to improve feasibility.
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页码：587 / 595
页数：8
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