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Almost sure exponential synchronization of drive-response stochastic memristive neural networks
Applied Mathematics and Computation ( IF 3.092 ) Pub Date : 2020-05-21 , DOI: 10.1016/j.amc.2020.125360
Siya Chen; Jianwen Feng; Jingyi Wang; Yi Zhao

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.
更新日期:2020-05-21

 

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