A new approach to exponential stability analysis of neural networks with time-varying delays

A new approach to exponential stability analysis of neural networks with time-varying delays
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DOI：
10.1016/j.neunet.2005.05.005
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发表时间：
2006
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
--
通讯作者：
Shengyuan Xu;J. Lam
Shengyuan Xu;J. Lam
中科院分区：
其他
文献类型：
--
作者：
Shengyuan Xu;J. Lam

文献摘要

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研究具有时变时滞的神经网络的指数稳定性分析问题。假设激活函数是全局Lipschitz连续的。利用线性矩阵不等式方法，推导了时滞神经网络具有全局指数稳定的唯一平衡点的充分条件。最近开发的求解LMI的算法可以很容易地检查所提出的LMI条件。通过实例证明了所提结果的保守性有所降低。
This paper considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results.