Synchronization Analysis for Stochastic Inertial Memristor-Based Neural Networks with Linear Coupling

Lixia Ye, Yonghui Xia, Jin-liang Yan and Haidong Liu

Complexity, 2020, vol. 2020, 1-14

Abstract:

This paper concerns the synchronization problem for a class of stochastic memristive neural networks with inertial term, linear coupling, and time-varying delay. Based on the interval parametric uncertainty theory, the stochastic inertial memristor-based neural networks (IMNNs for short) with linear coupling are transformed to a stochastic interval parametric uncertain system. Furthermore, by applying the Lyapunov stability theorem, the stochastic analysis approach, and the Halanay inequality, some sufficient conditions are obtained to realize synchronization in mean square. The established criteria show that stochastic perturbation is designed to ensure that the coupled IMNNs can be synchronized better by changing the state coefficients of stochastic perturbation. Finally, an illustrative example is presented to demonstrate the efficiency of the theoretical results.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5430410

DOI: 10.1155/2020/5430410

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