Finite-Time Synchronization of Neural Networks With Infinite Discrete Time-Varying Delays and Discontinuous Activations

被引：24

作者：

Sheng, Yin ^{[1
,2
]}

Zeng, Zhigang ^{[1
,2
]}

Huang, Tingwen ^{[3
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China

[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China

[3] Texas A&M Univ Qatar, Sci Program, Doha, Qatar

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 06期

关键词：

Artificial neural networks; Synchronization; Delays; Stability criteria; State feedback; Neurons; Asymptotic stability; Discontinuous activation; finite-time synchronization; infinite delay; neural networks (NNs); GLOBAL EXPONENTIAL STABILITY; ANTI-SYNCHRONIZATION; STABILIZATION; CONVERGENCE; SYSTEMS; DESIGN;

D O I：

10.1109/TNNLS.2021.3110880

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 investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

引用

页码：3034 / 3043

页数：10

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