Anti-Synchronization for Complex-Valued Bidirectional Associative Memory Neural Networks With Time-Varying Delays

Xiaofeng Wei,Meijuan Liu,Maiying Zhong,Zhen Wang,Ziye Zhang

doi:10.1109/access.2019.2928597

Abstract

Anti-Synchronization for Complex-Valued Bidirectional Associative Memory Neural Networks With Time-Varying Delays

Highlights

The bidirectional associative memory (BAM) neural networks model was first proposed by Kosko [1] in 1987, which contains two layers of neurons represented by FX layer and FY layer
Global exponential stability criterion is established in terms of LMIs for neutral delayed BAM neural networks with delays in leakage terms via new inequality technique in [12]
The main contributions of our work can be shown in the following points: (1) Compared with the previous results, it is the first time that the anti-synchronization control problem of complex-valued BAM neural networks with time-varying delays is investigated

Summary

INTRODUCTION

The bidirectional associative memory (BAM) neural networks model was first proposed by Kosko [1] in 1987, which contains two layers of neurons represented by FX layer and FY layer. For complex-valued BAM neural networks with time-varying delays, lagrange exponential stability is investigated by combining the Lyapunov function approach with some inequalities techniques in [29]. The exponential input-to-state stability for delayed complex-valued memristor-based BAM neural networks model is considered in [34]. For complex-valued BAM neural networks models, many researchers are interested in the synchronization problem of complex-valued BAM neural networks and have achieved some results. According to the discussions above, we know that it is necessary to investigate the anti-synchronization control problem of complex-valued BAM neural networks. The main contributions of our work can be shown in the following points: (1) Compared with the previous results, it is the first time that the anti-synchronization control problem of complex-valued BAM neural networks with time-varying delays is investigated. The main contributions of our work can be shown in the following points: (1) Compared with the previous results, it is the first time that the anti-synchronization control problem of complex-valued BAM neural networks with time-varying delays is investigated. (2) Via a suitable Lyapunov functional and the inequality techniques, a sufficient condition is established to ensure the anti-synchronization of the considered system. (3) According to Hölder inequality, the right inequalities different from those in the existing references are used to derive the main result

PROBLEM FORMULATION AND PRELIMINARIES

NUMERICAL EXAMPLES

CONCLUSION