Cluster Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays

被引：9

作者：

Liu, Peng ^{[1
,2
]}

Xu, Minglin ^{[1
,2
]}

Sun, Junwei ^{[1
,2
]}

Wen, Shiping ^{[3
]}

机构：

[1] Zhengzhou Univ Light Ind, Henan Key Lab Informat Based Elect Appliances, Zhengzhou 450002, Peoples R China

[2] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China

[3] Univ Technol Sydney, Fac Engn & Informat Technol, Australian AI Inst, Ultimo, NSW 2007, Australia

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Synchronization; Fuzzy control; Delays; Time-varying systems; Adaptive control; Delay effects; Couplings; cluster synchronization; fractional-order recurrent neural networks (FNNs); time-varying delays; COMPLEX DYNAMICAL NETWORKS; ADAPTIVE SYNCHRONIZATION; DIRECTED NETWORKS; SYSTEMS;

D O I：

10.1109/TNNLS.2021.3121516

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article focuses on the cluster synchronization of multiple fractional-order recurrent neural networks (FNNs) with time-varying delays. Sufficient criteria are deduced for realizing cluster synchronization of multiple FNNs via a pinning control by applying an extended Halanay inequality applicable for time-delayed fractional-order differential equations. Moreover, an adaptive control applicable for the synchronization of fractional-order systems with time-varying delays is proposed, under which sufficient criteria are derived for realizing cluster synchronization of multiple FNNs with time-varying delays. Finally, two examples are presented to illustrate the effectiveness of the theoretical results.

引用

页码：4007 / 4018

页数：12

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