Global asymptotical stability for a class of non-autonomous impulsive inertial neural networks with unbounded time-varying delay

被引：12

作者：

Li, Hongfei ^{[1
]}

Zhang, Wei ^{[1
]}

Li, Chuandong ^{[1
]}

Zhang, Wanli ^{[1
]}

机构：

[1] Southwest Univ, Natl & Local Joint Engn Lab Intelligent Transmiss, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China

来源：

NEURAL COMPUTING & APPLICATIONS | 2019年 / 31卷 / 10期

关键词：

Global asymptotic stability; Unbounded time-varying delays; Impulse; Inertial neural networks; EXPONENTIAL STABILITY; SYNCHRONIZATION; PERIODICITY; EXISTENCE;

D O I：

10.1007/s00521-018-3498-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article is concerned with the global asymptotical stability of non-autonomous impulsive inertial neural networks with unbounded delay. A new impulsive differential delay inequality which involves unbounded and non-differential delay is established. Moreover, based on a new impulsive differential delay inequality, new analysis techniques can effectively avoid the difficulties caused by unbounded delay and impulses, and several novel delay-dependent inequalities are obtained to ensure the global stability of this model. In the end, three examples are given to claim the validity of theoretical analysis.

引用

页码：6757 / 6766

页数：10

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