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

页数：17

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