Finite-time Synchronization of Delayed Semi-Markov Neural Networks with Dynamic Event-triggered Scheme

被引：46
|
作者：
Jin, Yujing [1 ]
Qi, Wenhai [1 ,2 ]
Zong, Guangdeng [1 ]
机构：
[1] Qufu Normal Univ, Sch Engn, Rizhao 276826, Peoples R China
[2] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2021年 / 19卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Dynamic event-triggered scheme; finite-time synchronization; Lyapunov-Krasovskii functional; semi-Markov neural networks; STABILITY-CRITERIA; COMPLEX NETWORKS; JUMP SYSTEMS; STABILIZATION; INEQUALITY;
D O I：
10.1007/s12555-020-0348-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the finite-time synchronization (FTS) of semi-Markov neural networks (S-MNNs) with time-varying delay is presented. According to the Lyapunov stability theory, a mode-dependent Lyapunov-Krasovskii functional (LKF) is constructed. Compared with the traditional static event triggered scheme (ETS), a dynamic ETS is adopted to adjust the amount of data transmission and reduce the network burden. By using the general free-weighting matrix method (F-WMM) to estimate a single integral term, a less conservative conclusion is proposed in standard linear matrix inequalities (LMIs). Finally, under the comparison of the static ETS and the dynamic ETS, a simulation example verifies the superiority of this method.
引用
收藏
页码：2297 / 2308
页数：12
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