New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays

被引：135

作者：

Zhang, Guodong ^{[1
]}

Zeng, Zhigang ^{[2
]}

Hu, Junhao ^{[1
]}

机构：

[1] South Cent Univ Nationalities, Sch Math & Stat, Wuhan 430074, Hubei, Peoples R China

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

来源：

NEURAL NETWORKS | 2018年 / 97卷

基金：

美国国家科学基金会;

关键词：

Dissipativity; Global attractive set; Memristive inertial neural networks; Distributed delays; ASYMPTOTIC STABILITY; LAGRANGE STABILITY; PASSIVITY ANALYSIS; LMI APPROACH; DISCRETE; SYNCHRONIZATION; CRITERIA; MODELS;

D O I：

10.1016/j.neunet.2017.10.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems. Finally, numerical simulations are given to illustrate the effectiveness of obtained results. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：183 / 191

页数：9

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