LMI-based approach for asymptotically stability analysis of delayed neural networks
LMI-based approach for asymptotically stability analysis of delayed neural networks
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DOI：
10.1109/tcsi.2002.800842
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发表时间：
2002-08
影响因子：
5.1
通讯作者：
X. Liao;Guanrong Chen;E. Sánchez
中科院分区：
文献类型：
--
作者：
X. Liao;Guanrong Chen;E. Sánchez
本文给出了常时滞和变时滞神经网络渐近稳定的充分条件。利用泛函微分方程的Lyapunov-Krasovskii稳定性理论和线性矩阵不等式（LMI）方法研究了该问题。它显示了一些著名的结果可以改进和推广，在一个简单的方式。对于常时滞的情况，稳定性判据是时滞无关的;对于时变时滞的情况，稳定性判据是时滞相关的。所得结果比文献中已有的结果保守性更小，为时滞神经网络的稳定性提供了又一组判据。
This paper derives some sufficient conditions for asymptotic stability of neural networks with constant or time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed to investigate the problem. It shows how some well-known results can be refined and generalized in a straightforward manner. For the case of constant time delays, the stability criteria are delay-independent; for the case of time-varying delays, the stability criteria are delay-dependent. The results obtained in this paper are less conservative than the ones reported so far in the literature and provides one more set of criteria for determining the stability of delayed neural networks.