Almost periodic solutions of Cohen-Grossberg neural networks with time-varying delay and variable impulsive perturbations

被引：38

作者：

Bohner, Martin ^{[1
]}

Stamov, Gani Tr ^{[2
]}

Stamova, Ivanka M. ^{[2
]}

机构：

[1] Missouri S&T, Dept Math & Stat, Rolla, MO 65409 USA

[2] Univ Texas San Antonio, Dept Math, San Antonio, TX 78249 USA

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2020年 / 80卷

关键词：

Almost periodic functions; Perfect stability; Variable impulsive perturbations; Cohen-Grossberg neural networks; Lyapunov-Razumikhin method; Uncertain terms; GLOBAL EXPONENTIAL STABILITY; ANTIPERIODIC SOLUTIONS; SYNCHRONIZATION ANALYSIS; DIFFERENTIAL-EQUATIONS; ROBUST STABILITY; STABILIZATION; EXISTENCE; SYSTEMS;

D O I：

10.1016/j.cnsns.2019.104952

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we consider the problem of existence of almost periodic solutions of impulsive Cohen-Grossberg neural networks with time-varying delays. The impulses are not at fixed moments, but are realized when the integral curves of solutions meet given hypersurfaces, i.e., the investigated model is with variable impulsive perturbations. Sufficient conditions for perfect stability of almost periodic solutions are derived. The main results are obtained by employing the Lyapunov-Razumikhin method and a comparison principle. In addition, the obtained results are extended to the uncertain case, and robust stability of almost periodic solutions is also investigated. An example is considered to demonstrate the effectiveness of our results. (c) 2019 Elsevier B.V. All rights reserved.

引用

页数：14

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