Exponential stability of complex-valued memristor-based neural networks with time-varying delays

被引：71
|
作者：
Shi, Yanchao [1 ]
Cao, Jinde [2 ]
Chen, Guanrong [3 ]
机构：
[1] Southwest Petr Univ, Sch Sci, Chengdu 610500, Peoples R China
[2] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[3] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2017年 / 313卷
基金：
中国国家自然科学基金;
关键词：
Memristor-based neural network; Complex-valued network; Matrix measure; Lyapunov-Krasovskii functional; Exponential stability; MATRIX MEASURE STRATEGIES; ANTI-SYNCHRONIZATION; DISSIPATIVITY;
D O I：
10.1016/j.amc.2017.05.078
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we propose a new type of complex-valued memristor-based neural networks with time-varying delays and discuss their exponential stability. Firstly, by using a matrix measure method, the Halanay inequality and some analytic techniques, we derive a sufficient condition for the global exponential stability of this type of neural networks. Then, we build a Lyapunov functional and utilize the Halanay inequality to establish several criteria for the exponential stability of such networks with time-varying delays. Finally, we show two numerical simulations to demonstrate the theoretical results. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码：222 / 234
页数：13
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