On robust stability of uncertain stochastic neural networks with distributed and interval time-varying delays
On robust stability of uncertain stochastic neural networks with distributed and interval time-varying delays
复制标题
DOI：
10.1016/j.chaos.2009.03.141
复制
发表时间：
2009-11
影响因子：
7.8
通讯作者：
W. Feng;Simon X. Yang;Haixia Wu
中科院分区：
文献类型：
--
作者：
W. Feng;Simon X. Yang;Haixia Wu
研究了具有分布时滞和区间时滞的随机不确定神经网络的鲁棒渐近稳定性问题。利用随机分析的方法，通过引入自由权矩阵和适当的考虑时滞范围的李雅普诺夫泛函，建立了时滞范围相关和速率相关的稳定性判据，以保证时滞神经网络在均方上鲁棒渐近稳定.新的准则对快变时滞和慢变时滞都适用。三个数值例子也被用来证明的主要结果的有用性。
This paper is concerned with the robust asymptotic stability analysis problem for stochastic uncertain neural networks with distributed and interval time-varying delays. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which take into account the ranges of delays, some new delay-range-dependent and rate-dependent stability criteria are established in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural networks to be robustly asymptotically stable in the mean square. And the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples have also been used to demonstrate the usefulness of the main results.