[期刊论文]


Sampled-data synchronisation for memristive neural networks with multiple time-varying delays via extended convex combination method

作   者:
Ruimei Zhang;Deqiang Zeng;Shouming Zhong;Yongbin Yu;Jun Cheng;

出版年:2018

页     码:922 - 932
出版社:Institution of Engineering and Technology (IET)


摘   要:

This study presents a rigorous mathematical framework for the global asymptotic synchronisation of memristive neural networks comprising multiple time-varying delays (MTVDs) through sampled-data control. First, a novel Lyapunov–Krasovskii functional (LKF) is constructed with some new terms, which can fully capture the information on lower and upper bounds of each MTVD. Second, extended convex combination method is presented, which can successfully solve the combination of MTVDs. Third, based on the LKF and employing the extended convex combination technique, synchronisation criterion is derived. In comparison with existing results, the established criterion is more appropriate since it fully utilises the lower and upper bounds of each MTVD. Finally, simulation results are presented to validate the theoretical models.



关键字:

MTVD; global asymptotic synchronisation; sampled-data control; sampled-data synchronisation; Lyapunov-Krasovskii functional; LKF; synchronisation criterion; memristive neural networks; multiple time-varying delays; extended convex combination method


所属期刊
IET Control Theory & Applications
ISSN: 1751-8644
来自:Institution of Engineering and Technology (IET)