Almost sure exponential synchronization of drive-response stochastic memristive neural networks

Siya Chen, Jianwen Feng, Jingyi Wang and Yi Zhao

Applied Mathematics and Computation, 2020, vol. 383, issue C

Abstract: This paper concerns with the almost sure exponential synchronization for some general classes of drive-response stochastic memristive neural networks (SMNNs) with nonidentical nodes under state feedback controllers. The SMNNs considered may include networks which are asymmetrically nondelayed and delayed coupled simultaneously, and state-dependent or even those that are subject to exogenous stochastic perturbations representatively. The main results of this paper are a collection of generic sufficient conditions for guaranteed almost sure exponential synchronization of these SMNNs, which performs great advantages compared with mean-square synchronization. Furthermore, some practical corollaries are also obtained from the main results that may be directly applied to some smaller subclasses of these networks. In particular, a simpler and more effective way of almost surely exponentially synchronizing SMNNs without delays follows by considering them as a special case of SMNNs with delays. Some numerical simulations are given to illustrate our main theoretical findings.

Keywords: Almost sure exponential synchronization; Stochastic memristive neural networks; Feedback control; Time-varying delay (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:383:y:2020:i:c:s0096300320303246

DOI: 10.1016/j.amc.2020.125360

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