Global Asymptotic Stability of Periodic Solutions for Neutral-Type BAM Neural Networks with Delays

被引：5

作者：

Gao, Dongdong ^{[1
]}

Li, Jianli ^{[1
]}

机构：

[1] Hunan Normal Univ, Coll Math & Stat, Minist Educ China, Key Lab High Performance Comp & Stochast Informat, Changsha 410081, Hunan, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2020年 / 51卷 / 01期

关键词：

Neutral-type; BAM neural networks; Continuation theorem; Periodic solutions; Global asymptotic stability; EXPONENTIAL STABILITY; HOMOCLINIC SOLUTIONS; EXISTENCE; SYSTEMS;

D O I：

10.1007/s11063-019-10092-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we study the neutral-type BAM neural networks with time-varying delays. By applying the continuation theorem and some analysis techniques, some sufficient conditions to guarantee the neutral-type BAM neural networks have at least one periodic solution are proposed. Moreover, we also consider the asymptotic behaviours of periodic solutions by Lyapunov function and inequality 2ab <= a(2) + b(2). At last, an example is given to illustrate the effectiveness and feasibility of the obtain results.

引用

页码：367 / 382

页数：16

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