Xu Shengyuan, Lam James
Department of Automation, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China.
Neural Netw. 2006 Jan;19(1):76-83. doi: 10.1016/j.neunet.2005.05.005. Epub 2005 Sep 8.
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.
本文考虑了具有时变延迟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。开发了一种线性矩阵不等式（LMI）方法来推导确保延迟神经网络具有唯一平衡点且全局指数稳定的充分条件。所提出的LMI条件可以通过最近开发的求解LMI的算法轻松检验。给出了例子以证明所提结果降低了保守性。
