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

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.

Inspec keywords: memristor circuits; asymptotic stability; time-varying systems; Lyapunov methods; synchronisation; neural nets; sampled data systems; combinatorial mathematics; delays

Other keywords: 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

Subjects: Stability in control theory; Discrete control systems; Time-varying control systems; Distributed parameter control systems; Neural nets (circuit implementations); Combinatorial mathematics; Neural nets (theory); Resistors; Combinatorial mathematics

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2017.1172
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