A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen-Grossberg neural networks

被引：97
作者：
Dong, Zeyu [1 ,2 ]
Wang, Xin [1 ,2 ]
Zhang, Xian [1 ,2 ]
机构：
[1] Heilongjiang Univ, Sch Math Sci, Harbin 150080, Peoples R China
[2] Heilongjiang Univ, Heilongjiang Prov Key Lab Theory & Computat Compl, Harbin 150080, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2020年 / 385卷 / 385期
基金：
中国国家自然科学基金;
关键词：
High-order neural networks; Global exponential stability; Nonsingular M-matrix; Multiple time-varying delays; PERIODIC-SOLUTION; H-INFINITY; VARYING DELAY; SYSTEMS; EXISTENCE; DESIGN;
D O I：
10.1016/j.amc.2020.125401
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper focuses on the problem of global exponential stability analysis for high-order delayed discrete-time Cohen-Grossberg neural networks. Multiple time-varying delays are considered. First, a technique lemma is obtained based on the properties of nonsingular M-matrices. Second, the delay-dependent and -independent criteria under which the zero equilibrium is globally exponentially stable are derived, respectively. Last, the validity of these criteria are illustrated by a pair of numerical examples. Compared with the previous results, the merits of the proposed method are: (i) no Lyapunov-Krasovskii functional or auxiliary function is required; (ii) less computational complexity is verified; and (iii) the obtained stability criteria can easily be realized, since they are to test whether a matrix is nonsingular M-matrix. (C) 2020 Elsevier Inc. All rights reserved.
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页数：11
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