Cluster synchronization for directed coupled inertial reaction-diffusion neural networks with nonidentical nodes via non-reduced order method

被引：9

作者：

Chen, Shanshan ^{[1
]}

Jiang, Haijun ^{[1
]}

Hu, Cheng ^{[1
]}

Li, Liang ^{[2
]}

机构：

[1] Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830046, Peoples R China

[2] Yili Normal Univ, Sch Math & Stat, Yining 835000, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2023年 / 360卷 / 04期

关键词：

EXPONENTIAL SYNCHRONIZATION; LINEAR-SYSTEMS; STABILITY; CHAOS; STABILIZATION;

D O I：

10.1016/j.jfranklin.2022.12.049

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The cluster synchronization issues are investigated for directed coupled inertial reaction-diffusion neural networks (CIRDNNs) with nonidentical nodes by imposing two effective pinning control. A novel Lyapunov-Krasovskii functional (LKF) is established to directly analyze the dynamic behavior of CIRDNNs and deal with reaction-diffusion term, inertia term and coupling term. Moreover, based on different desired cluster synchronization states including a set of un-decoupled trajectories and the particular solutions of the decoupled node systems, two class of synchronization criteria in view of algebraic inequalities are derived under two different communication topologies, respectively. Finally, two typical examples are given to verify the theoretical results. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

引用

页码：3208 / 3240

页数：33

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