Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control

被引：166
作者：
Wen, Shiping [1 ]
Zeng, Zhigang [1 ]
Chen, Michael Z. Q. [2 ]
Huang, Tingwen [3 ]
机构：
[1] Huazhong Univ Sci & Technol, Guangdong HUST Ind Technol Res Inst, Guangdong Prov Key Lab Digital Mfg Equipment,Sch, Key Lab Image Proc & Intelligent Control,Educ Min, Wuhan 430074, Hubei, Peoples R China
[2] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China
[3] Texas A&M Univ Qatar, Doha 5825, Qatar
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2017年 / 28卷 / 10期
基金：
澳大利亚研究理事会; 中国国家自然科学基金;
关键词：
Event-triggered control; switched neural network; synchronization; GLOBAL EXPONENTIAL SYNCHRONIZATION; DEPENDENT ROBUST STABILITY; ADAPTIVE SYNCHRONIZATION; LAG SYNCHRONIZATION; MULTIAGENT SYSTEMS; TIME-DELAY; MODEL; IDENTIFICATION; STABILIZATION; DESIGN;
D O I：
10.1109/TNNLS.2016.2580609
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 addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.
引用
收藏
页码：2334 / 2343
页数：10
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