Improved delay-dependent stability analysis of discrete-time neural networks with time-varying delay

被引：41
|
作者：
Jin, Li [1 ]
He, Yong [1 ]
Wu, Min [1 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2017年 / 354卷 / 04期
基金：
中国国家自然科学基金;
关键词：
FINITE-SUM INEQUALITY; INTEGRAL INEQUALITY; SYSTEMS; CRITERIA; STABILIZATION;
D O I：
10.1016/j.jfranklin.2016.12.027
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 presents two improved delay-dependent stability criteria for discrete-time neural networks with time-varying delay. First, a Lyapunov Krasovskii functional (LKF) with several augmented terms is constructed. Then an improved summation inequality, together with Wirtinger-based inequality, is employed to give tight estimations for sum terms in the forward difference of the LKF. Moreover, two methods for handling the time-varying delay information are applied. As a result, two stability criteria in terms of linear matrix inequality are established. Finally, two numerical examples are given to demonstrate the effectiveness and benefits of the developed stability criteria. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：1922 / 1936
页数：15
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