Wu Zhengguang, Su Hongye, Chu Jian, Zhou Wuneng
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, Zhejiang, China.
IEEE Trans Neural Netw. 2010 Apr;21(4):692-7. doi: 10.1109/TNN.2010.2042172. Epub 2010 Feb 17.
This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of variables than the existing ones. Numerical example is given to demonstrate the effectiveness of the proposed method.
本文简要研究了具有时变延迟的离散递归神经网络的全局指数稳定性分析问题。基于线性矩阵不等式（LMI）方法，通过一个新的李雅普诺夫函数为所考虑的递归神经网络建立了一个新的时滞相关稳定性准则。与现有准则相比，所得到的条件具有更小的保守性和更少的变量数量。给出了数值例子以证明所提方法的有效性。
