Event-triggered synchronization of discrete-time neural networks: A switching approach

被引：121

作者：

Ding, Sanbo ^{[1
]}

Wang, Zhanshan ^{[2
]}

机构：

[1] Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300401, Peoples R China

[2] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China

来源：

NEURAL NETWORKS | 2020年 / 125卷

基金：

中国国家自然科学基金;

关键词：

Discrete-time neural networks; Synchronization; Event-triggered control; Switching method; Actuator saturation; INFINITY STATE ESTIMATION; SAMPLED-DATA; EXPONENTIAL SYNCHRONIZATION; CHAOTIC SYSTEMS; STABILITY; DELAY; STABILIZATION; PARAMETERS; DYNAMICS; SUBJECT;

D O I：

10.1016/j.neunet.2020.01.024

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 investigates the event-triggered synchronization control of discrete-time neural networks. The main highlights are threefold: (1) a new event-triggered mechanism (ETM) is presented, which can be regarded as a switching between the discrete-time periodic sampled-data control and a continuous ETM; (2) a saturating controller which is equipped with two switching gains is designed to match the switching property of the proposed ETM; (3) a dedicated switching Lyapunov-Krasovskii functional is constructed, which takes the sawtooth constraints of control input into account. Based on these ingredients, the synchronization criteria are derived such that the considered error systems are locally stable. Whereafter, two co-design problems are discussed to maximize the set of admissible initial conditions and the triggering threshold, respectively. Finally, the effectiveness and advantages of the proposed method are validated by two numerical examples. (c) 2020 Elsevier Ltd. All rights reserved.

引用

页码：31 / 40

页数：10

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