Improved exponential stability criteria for recurrent neural networks with time-varying discrete and distributed delays

被引：10
|
作者：
Wu Y.-Y. [1 ]
Li T. [2 ]
Wu Y.-Q. [3 ]
机构：
[1] School of Automation, Southeast University
[2] Department of Information and Communication, Nanjing University of Information Science and Technology
[3] Institute of Automation, Qufu Normal University
来源：
International Journal of Automation and Computing | 2010年 / 7卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Exponential stability; Linear matrix inequalities (LMIs); Neural networks; Time-varying delay;
D O I：
10.1007/s11633-010-0199-z
中图分类号：
学科分类号：
摘要：
In this paper, the problem of the global exponential stability analysis is investigated for a class of recurrent neural networks (RNNs) with time-varying discrete and distributed delays. Due to a novel technique when estimating the upper bound of the derivative of Lyapunov functional, we establish new exponential stability criteria in terms of LMIs. It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to show the effectiveness of the proposed results. © 2010 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：199 / 204
页数：5
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