Periodicity and dissipativity for memristor-based mixed time-varying delayed neural networks via differential inclusions

被引：47

作者：

Duan, Lian ^{[1
]}

Huang, Lihong ^{[1
,2
]}

机构：

[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China

[2] Hunan Womens Univ, Dept Informat Technol, Changsha 410004, Hunan, Peoples R China

来源：

NEURAL NETWORKS | 2014年 / 57卷

基金：

中国国家自然科学基金;

关键词：

Memristor-based neural networks; Mawhin-like coincidence theorem; Periodicity; Dissipativity; GLOBAL EXPONENTIAL STABILITY; DISCONTINUOUS ACTIVATIONS; CIRCUIT; SYNCHRONIZATION; CONVERGENCE;

D O I：

10.1016/j.neunet.2014.05.002

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, we investigate a class of memristor-based neural networks with general mixed delays involving both time-varying delays and distributed delays. By using the Mawhin-like coincidence theorem, together with the differential inclusion theory, M-matrix properties and differential inequality techniques, some novel criteria are established for ensuring the periodicity and dissipativity for the addressed neural networks. Finally, two numerical examples with simulations are presented to demonstrate the effectiveness of the theoretical results. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：12 / 22

页数：11

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