Pinning Synchronization via Intermittent Control for Memristive Cohen-Grossberg Neural Networks With Mixed Delays

Meng Hui,Qisheng Wu,Lin Bai,Herbert Ho-Ching Iu,Rui Yao,Ni Luo

doi:10.1109/access.2020.2982043

Meng Hui, Qisheng Wu + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.2982043

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 47	License type: CC BY 4.0

Affiliation: Chang'an University, University of Western Australia

Abstract

This paper presents the exponential synchronization for a class of memristive Cohen-Grossberg neural networks (MCGNNs) with mixed delays via a new hybrid control strategy. This new hybrid control strategy combines pinning control and periodic intermittent control. According to the feature of memristor, the memristive terms of the MCGNNs with mixed delays are normalized by a simple linear transformation. Then the designed periodic intermittent control is added to selected partial network nodes. Based on the stability theory of memristive neural networks and the exponential synchronization rule, the new synchronization conditions are given. Finally, numerical simulations are provided to show the effectiveness of the theoretical method.

Highlights

In the past few decades, neural networks have been extensively studied in such diverse fields as associative memory, classification, parallel computation, pattern recognition, signal processing, decision aid, and artificial intelligence [1]–[4]
To expand synchronization researches on memristive neural networks, it is of great significance on the synchronization on memristive Cohen-Grossberg neural networks (MCGNNs) with mixed delays by a hybrid control
The periodic intermittent control is applied to n nodes networks to achieve the synchronization between drive networks (2) and response networks (14)

Summary

INTRODUCTION

In the past few decades, neural networks have been extensively studied in such diverse fields as associative memory, classification, parallel computation, pattern recognition, signal processing, decision aid, and artificial intelligence [1]–[4]. Abdurahman et al [23] studied that the exponential lag synchronization of CGNNs with mixed time-delays via periodically intermittent control. In [53], the exponential synchronization of memristive neural networks with time-varying delays was proposed via designing a pinning aperiodic intermittent control. By selecting part of the MCGNNs with mixed delays for periodic intermittent control, the exponential synchronization of the networks are achieved. According to changing the control parameter and amplification function, the exponential synchronization of the MCGNNs with timevarying delays or the memristive neural networks with timevarying delays or mixed delays can be achieved. To expand synchronization researches on memristive neural networks, it is of great significance on the synchronization on MCGNNs with mixed delays by a hybrid control.

PRELIMINARY

NUMERICAL SIMULATION

CONCLUSION