Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances

被引：13

作者：

Luo, Weiwei ^{[1
]}

Zhong, Kai ^{[1
]}

Zhu, Song ^{[1
]}

Shen, Yi ^{[2
]}

机构：

[1] China Univ Min & Technol, Coll Sci, Xuzhou 221116, Peoples R China

[2] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China

来源：

NEURAL NETWORKS | 2014年 / 53卷

基金：

中国国家自然科学基金;

关键词：

Recurrent neural networks; Global exponential stability; Time delays; Random disturbances; Adjustable parameters; VARYING DELAYS; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; STABILIZATION; CRITERION; DISCRETE; MATRICES;

D O I：

10.1016/j.neunet.2014.02.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, further results on robustness analysis of global exponential stability of recurrent neural networks (RNNs) subjected to time delays and random disturbances are provided. Novel exponential stability criteria for the RNNs are derived, and upper bounds of the time delay and noise intensity are characterized by solving transcendental equations containing adjustable parameters. Through the selection of the adjustable parameters, the upper bounds are improved. It shows that our results generalize add improve the corresponding results of recent works. In addition, some numerical examples are given to show the effectiveness of the results we obtained. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：127 / 133

页数：7

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