Exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks

被引：5

作者：

Jiang, Wenlin ^{[1
]}

Li, Liangliang ^{[1
]}

Tu, Zhengwen ^{[1
]}

Feng, Yuming ^{[2
]}

机构：

[1] Chongqing Three Gorges Univ, Sch Math & Stat, Chongqing 404100, Peoples R China

[2] Chongqing Three Gorges Univ, Key Lab Intelligent Informat Proc & Control, Chongqing Municipal Inst Higher Educ, Chongqing, Peoples R China

来源：

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 2019年 / 50卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Discrete-time; impulses and delays; Lagrange stability; recurrent neural networks; GLOBAL STABILITY; VARYING DELAYS;

D O I：

10.1080/00207721.2018.1543475

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on the problem of exponential stability in the sense of Lagrange for impulses in discrete-time delayed recurrent neural networks. By establishing a delayed impulsive discrete inequality and a novel difference inequality, combining with inequality techniques, some novel sufficient conditions are obtained to ensure exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks. Meanwhile, exponentially convergent scope of neural network is given. Finally, several numerical simulations are given to demonstrate the effectiveness of our results.

引用

页码：50 / 59

页数：10

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