Adaptive Event-Triggered Synchronization of Reaction-Diffusion Neural Networks

被引：39

作者：

Zhang, Ruimei ^{[1
]}

Zeng, Deqiang ^{[2
,3
]}

Park, Ju H. ^{[4
]}

Liu, Yajuan ^{[5
]}

Xie, Xiangpeng ^{[6
]}

机构：

[1] Sichuan Univ, Coll Cybersecur, Chengdu 610065, Peoples R China

[2] Sichuan Normal Univ, Sch Math Sci, Chengdu 610066, Peoples R China

[3] Neijiang Normal Univ, Data Recovery Key Lab Sichuan Prov, Neijiang 641100, Peoples R China

[4] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea

[5] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China

[6] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210003, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2021年 / 32卷 / 08期

基金：

中国国家自然科学基金; 新加坡国家研究基金会;

关键词：

Delays; Synchronization; Delay effects; Artificial neural networks; Adaptive systems; Time-varying systems; Adaptive event-triggered mechanism; random time-varying delays; reaction-diffusion neural networks (RDNNs); sampled-data control; LOAD FREQUENCY CONTROL; H-INFINITY; TIME DELAYS; SYSTEMS; STABILITY;

D O I：

10.1109/TNNLS.2020.3027284

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 design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

引用

页码：3723 / 3735

页数：13

共 48 条

[1]

[Anonymous], 1993, NEURAL NETWORKS OPTI

[2] Reaction-diffusion CNN algorithms to generate and control artificial locomotion [J].

Arena, P ;

Fortuna, L ;

Branciforte, M .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS, 1999, 46 (02) :253-260

[3] Exponential Synchronization of Coupled Stochastic Memristor-Based Neural Networks With Time-Varying Probabilistic Delay Coupling and Impulsive Delay [J].

Bao, Haibo ;

Park, Ju H. ;

Cao, Jinde .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (01) :190-201

[4]

BELLEISLE AP, 1975, IEEE T AUTOMAT CONTR, VAC20, P67

[5] Synchronization control for reaction-diffusion FitzHugh-Nagumo systems with spatial sampled-data [J].

Chen, Sheng ;

Lim, Cheng-Chew ;

Shi, Peng ;

Lu, Zhenyu .

AUTOMATICA, 2018, 93 :352-362

[6] Impulsive Synchronization of Reaction-Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption [J].

Chen, Wu-Hua ;

Luo, Shixian ;

Zheng, Wei Xing .

IEEE Transactions on Neural Networks and Learning Systems, 2016, 27 (12) :2696-2710

[7] CELLULAR NEURAL NETWORKS - APPLICATIONS [J].

CHUA, LO ;

YANG, L .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1988, 35 (10) :1273-1290

[8] A refined input delay approach to sampled-data control [J].

Fridman, Emilia .

AUTOMATICA, 2010, 46 (02) :421-427

[9] Decentralized Adaptive Event-Triggered H∞ Filtering for a Class of Networked Nonlinear Interconnected Systems [J].

Gu, Zhou ;

Shi, Peng ;

Yue, Dong ;

Ding, Zhengtao .

IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (05) :1570-1579

[10] On designing of an adaptive event-triggered communication scheme for nonlinear networked interconnected control systems [J].

Gu, Zhou ;

Yue, Dong ;

Tian, Engang .

INFORMATION SCIENCES, 2018, 422 :257-270

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