Synchronization of a Class of Memristive Stochastic Bidirectional Associative Memory Neural Networks with Mixed Time-Varying Delays via Sampled-Data Control

Manman Yuan,Lixiang Li,Wenbing Zhao,Chao Ge,Weiping Wang,Jürgen Kurths,Xiong Luo

doi:10.1155/2018/9126183

Abstract

The paper addresses the issue of synchronization of memristive bidirectional associative memory neural networks (MBAMNNs) with mixed time-varying delays and stochastic perturbation via a sampled-data controller. First, we propose a new model of MBAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying distributed delays and discrete delays. Second, we design a new method of sampled-data control for the stochastic MBAMNNs. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the methods are carefully designed to confirm the synchronization processes are suitable for the feather of the memristor. Third, sufficient criteria guaranteeing the synchronization of the systems are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

Highlights

Associate memory is one of the most significant activities of human brain, which can be applied in study of brain-like systems [1], intelligent thinking for intelligent robots [2], and so on
Motivated by the foregoing discussions, this paper aims at investigating the synchronization of memristive bidirectional associative memory neural networks (MBAMNNs) with mixed time-varying delays and stochastic perturbations by designing a suitable sampled-data controller
We get some new sufficient conditions to ensure the synchronization of MBAMNNs by the designed sampled-data controller

Summary

Introduction

Associate memory is one of the most significant activities of human brain, which can be applied in study of brain-like systems [1], intelligent thinking for intelligent robots [2], and so on. Since Kosko discussed the concept of bidirectional associative memory neural networks (BAMNNs) [3] in 1988, BAMNNs occupied the great researchers’ time and have been studied for several years. Due to the wide applications in signal processing, associative memory, pattern recognition, and so on [4], chaos control and synchronization of BAMNNs have been intensively investigated. MBAMNNs are more suitable for mimicking the associative memory process of human brain contrast with the BAMNNs. more and more researchers build the MBAMNNs models for investigating a variety of applications [7,8,9,10]

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Conclusion