Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects
Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects
复制标题
具有时变延迟和脉冲效应的复值神经网络的全局指数稳定性
DOI：
10.1016/j.neunet.2016.03.007
复制
发表时间：
2016-07-01
期刊：
影响因子：
7.8
通讯作者：
Liu, Yurong
中科院分区：
文献类型：
--
作者：
Song, Qiankun;Yan, Huan;Liu, Yurong
本文讨论了具有时变时滞和脉冲效应的复值神经网络的全局指数稳定性问题。利用Lyapunov泛函方法和矩阵不等式技术，得到了复值线性矩阵不等式形式下神经网络平衡点存在唯一性和全局指数稳定性的几个充分条件。此外，还估计了与系统参数有关的指数收敛率指标。本文提出的稳定性结果比文献中一些已知的稳定性结果保守性更小，并通过两个算例进行了仿真验证。(C) 2016 Elsevier Ltd.版权所有。
In this paper, the global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects is discussed. By employing Lyapunov functional method and using matrix inequality technique, several sufficient conditions in complex-valued linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the considered neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed stability results are less conservative than some recently known ones in the literatures, which is demonstrated via two examples with simulations. (C) 2016 Elsevier Ltd. All rights reserved.