Open AccessSampled‐data synchronisation for memristive neural networks with multiple time‐varying delays via extended convex combination method
Author(s) -
Zhang Ruimei,
Zeng Deqiang,
Zhong Shouming,
Yu Yongbin,
Cheng Jun
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.1172
Subject(s) - convex combination , artificial neural network , control theory (sociology) , upper and lower bounds , computer science , regular polygon , mathematics , convex optimization , mathematical optimization , algorithm , control (management) , artificial intelligence , geometry , mathematical analysis
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.