Event-Triggered Synchronization Strategy for Multiple Neural Networks With Time Delay

被引：42
|
作者：
Chen, Jiejie [1 ]
Chen, Boshan [2 ]
Zeng, Zhigang [3 ,4 ]
Jiang, Ping [5 ]
机构：
[1] Hubei Normal Univ, Coll Comp Sci & Technol, Huangshi 435002, Hubei, Peoples R China
[2] Hubei Normal Univ, Coll Math & Stat, Huangshi 435002, Hubei, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
[4] Huazhong Univ Sci & Technol, Minist China, Key Lab Image Proc & Intelligent Control Educ, Wuhan 430074, Peoples R China
[5] Hubei Polytech Univ, Comp Sch, Huangshi 435002, Hubei, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2020年 / 50卷 / 07期
关键词：
Synchronization; Couplings; Artificial neural networks; Nickel; Neurons; Laplace equations; Event-triggered; multiple neural networks (NNs); sampling coupled; synchronization; GLOBAL EXPONENTIAL PERIODICITY; COMPLEX NETWORKS; STABILITY; SYSTEMS; COMMUNICATION; CRITERIA;
D O I：
10.1109/TCYB.2019.2911029
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper deals with global exponential synchronization of multiple neural networks (NNs) with time delay via a very broad class of event-triggered coupling, in which coupling matrix can be non-Laplacian. Some simple and convenient sufficient conditions are derived to guarantee global exponential synchronization of the coupling NNs under an event-triggered strategy. In particular, the effect of the common subsystem can be positive or negative on the synchronization scheme. Three examples are presented to test the results in theory analysis.
引用
收藏
页码：3271 / 3280
页数：10
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