Exponential Stability of Markovian Jumping Memristor-Based Neural Networks via Event-Triggered Impulsive Control Scheme

被引：6

作者：

Yang, Nijing ^{[1
]}

Yu, Yongbin ^{[1
]}

Zhong, Shouming ^{[2
]}

Wang, Xiangxiang ^{[1
]}

Shi, Kaibo ^{[3
]}

Cai, Jingye ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 610054, Peoples R China

[3] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Artificial neural networks; Delays; Memristors; Control theory; Stability; Biological neural networks; Switches; Memristor-based neural networks; Markovian jumping; event-triggered impulsive control scheme; exponential stability; H-INFINITY CONTROL; SAMPLING CONTROL; SYSTEMS; SYNCHRONIZATION; STABILIZATION; DESIGN;

D O I：

10.1109/ACCESS.2020.2974040

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 studies the modeling and exponential stability problems for markovian jumping memristor-based neural networks (MJMNNs) via event-triggered impulsive control scheme (ETICS). The purpose is to design memristor-based neural networks (MNNs) which has markovian jumping parameters and hybrid time-vary delays to make the MNNs more general. Meanwhile, a state estimator is introduced to estimate system states through a vailable output measurements. Furthermore, the proposed event-triggered scheme (ETS), which is also determined by markovian parameters, is used to determine whether there is an impulse and whether the system need to transmit the sampled state information. Then, by using Lyapunov-Krasovskii functional (LKF) and an improved inequality, exponential stable criterions are established. Finally, a numerical example is given to support the results.

引用

页码：32564 / 32574

页数：11

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