Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed Delays

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 n...

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Bibliographic Details
Published inInternational journal of automation and computing Vol. 7; no. 2; pp. 199 - 204
Main Author Yuan-Yuan Wu Tao Li Yu-Qiang Wu
Format Journal Article
LanguageChinese
Published 2010
Subjects
分布时滞
时变
李雅普诺夫
离散
稳定性分析
稳定性判据
线性矩阵不等式
递归神经网络
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Summary: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.
Bibliography:TP18
O175
Neural networks, time-varying delay, exponential stability, linear matrix inequalities （LMIs）.
11-5350/TP
ISSN:1476-8186
1751-8520