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

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.

Quasi-Synchronization of Nonidentical Fractional-Order Memristive Neural Networks via Impulsive Control

“…Memristor was predicted as the fourth circuit element describing the relationship between magnetic flux and voltage by professor Chua [1] in 1971. is component was established successfully by HP Laboratories [2,3] in 2008. Memristors are used instead of traditional resistive elements to simulate brain neuron synapses and build the memristor neural networks (MNNs) model [4][5][6][7][8][9] because they have memory characteristics. Now, it has been widely used in the field of information processing, associative memory, and image processing [10][11][12].…”