Event-triggered finite-time synchronization for uncertain neural networks with quantizations

被引:2
|
作者
Zhang, Yingqi [1 ]
Li, Xiao [1 ]
Yan, Jingjing [2 ]
机构
[1] Henan Univ Technol, Sch Sci, Lianhua St, Zhengzhou 450001, Peoples R China
[2] Henan Univ Technol, Coll Elect Engn, Lianhua St, Zhengzhou 450001, Peoples R China
来源
COMPUTATIONAL & APPLIED MATHEMATICS | 2022年 / 41卷 / 05期
基金
中国国家自然科学基金;
关键词
Neural networks; Event-triggered mechanism; Variable separation method; Quantization; Finite-time synchronization; SYSTEMS;
D O I
10.1007/s40314-022-01904-4
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper concentrates on the problem of event-triggered finite-time quantized synchronization for uncertain neural networks. We initially establish a synchronization model for neural networks with input quantization and event-triggered mechanism. Then, the finite-time synchronization criterion for the neural networks is deduced by Lyapunov functional and free-weighting matrix schemes. Utilizing variable separation approach, finite-time synchronization controller and event-triggered matrix parameter are co-designed in terms of the feasibility of linear matrix inequalities. Additionally, the effectiveness of the proposed methods is evaluated by a simulation example.
引用
收藏
页数:17
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