On synchronization of competitive memristor-based neural networks by nonlinear control

被引：9

作者：

Zheng, Cheng-De ^{[1
]}

Zhang, Lulu ^{[1
]}

机构：

[1] Dalian Jiaotong Univ, Sch Sci, Dalian 116028, Peoples R China

来源：

NEUROCOMPUTING | 2020年 / 410卷

基金：

中国国家自然科学基金;

关键词：

Neural networks (NNs); Synchronization; Delay-product function; Legendre polynomials (LPs); Quadratic function; STABILITY ANALYSIS; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; SYSTEMS; DELAYS; INEQUALITY; DISCRETE; CRITERIA;

D O I：

10.1016/j.neucom.2020.05.061

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper probes into the synchronization of delayed competitive memristor-based neural networks (CMNNs). Firstly, a new loosened integral inequality is advanced to decrease the estimation difference of auxiliary function-based integral inequalities (AFBIs). Next, by utilizing Legendre polynomials and appropriate loose variables, a delay-product function (DPF) is advanced to employ extra freedom and more information on system states. Then, by proposing nonlinear controller and Lyapunov-Krasovskii functional (LKF) based on above DPF, two delay-dependent principles are offered to accomplish the global synchronization by making use of Chen et al.'s integral inequalities and quadratic combination technique. Finally, an example is supplied to reveal the effectiveness of the presented results. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：151 / 160

页数：10

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