Global exponential stability of impulsive high-order BAM neural networks with time-varying delays
Global exponential stability of impulsive high-order BAM neural networks with time-varying delays
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DOI：
10.1016/j.neunet.2006.02.006
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发表时间：
2006-12-01
期刊：
影响因子：
7.8
通讯作者：
Lam, James
中科院分区：
文献类型：
--
作者：
Ho, Daniel W. C.;Liang, Jinling;Lam, James
本文研究了一类具有时变时滞的脉冲高阶双向联想记忆（BAM）神经网络的全局指数稳定性和指数收敛性。通过运用线性矩阵不等式（LMI）以及含有时滞和脉冲的微分不等式，得到了确保系统全局指数稳定的几个充分条件。本文结尾还给出了三个说明性的例子以展示我们结果的有效性。（c）2006爱思唯尔有限公司。保留所有权利。
In this paper, global exponential stability and exponential convergence are studied for a class of impulsive high-order bidirectional associative memory (BAM) neural networks with time-varying delays. By employing linear matrix inequalities (LMIs) and differential inequalities with delays and impulses, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Three illustrative examples are also given at the end of this paper to show the effectiveness of our results. (c) 2006 Elsevier Ltd. All rights reserved.