Exponential stabilisation of memristive neural networks under intermittent output feedback control

被引：7

作者：

Li, Xiaofan ^{[1
,2
]}

Fang, Jian-an ^{[2
]}

Li, Huiyuan ^{[1
]}

机构：

[1] Yancheng Inst Technol, Sch Elect Engn, Yancheng, Peoples R China

[2] Donghua Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China

来源：

INTERNATIONAL JOURNAL OF CONTROL | 2018年 / 91卷 / 08期

关键词：

Memristive time-varying delayed neural networks; exponential stabilisation; intermittent control; TIME-VARYING DELAYS; COMPLEX NETWORKS; SYNCHRONIZATION; STABILITY; DISCRETE; SYSTEMS; MODEL;

D O I：

10.1080/00207179.2017.1333155

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 exponential stabilisation problem is studied for a general class of memristive time-varying delayed neural networks under periodically intermittent output feedback control. First, the periodically intermittent output feedback control rule is designed for the exponential stabilisation of the memristive time-varying delayed neural networks. Then, we derive stabilisation criteria so that the memristive time-varying delayed neural networks are exponentially stable. By the mathematical induction method and constructing suitable Lyapunov-Krasovskii functionals, some easy-to-check criteria are obtained to ensure the exponential stabilisation of memristive time-varying delayed neural networks. Finally, two numerical simulation examples are given to illustrate the validity of the obtained results.

引用

页码：1848 / 1860

页数：13

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