Finite-Time Synchronization of Memristor-Based Fractional Order Cohen-Grossberg Neural Networks

Meng Hui,Jiao Zhang,Lin Bai,Rui Yao,Herbert Ho-Ching Iu,Ni Luo,Chen Wei

doi:10.1109/access.2020.2988520

Meng Hui, Jiao Zhang + Show 5 more

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https://doi.org/10.1109/access.2020.2988520

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 11	License type: CC BY 4.0

Affiliation: Chang'an University, University of Western Australia

Abstract

This paper is concerned with the finite-time synchronization (FTS) of memristor-based fractional order Cohen-Grossberg neural networks (MFCGNNs) with time-varying delays. Under the frame of fractional order differential inclusion and set-valued map, some new sufficient conditions to guarantee the FTS of MFCGNNs are established by means of constructing two different Lyapunov functions based on L1-norm in Theorem 1 and Lp-norm in Theorem 2. Via applying the asymptotic expansion property of Mittag-Leffler function, we propose a new estimation method of the settling time for synchronization which is less conservative than previous researches. Meanwhile, we deeply discuss the influence factor of settling time for synchronization. Finally, two numerical examples are given to demonstrate the effectiveness of obtained results.

Highlights

Fractional order calculus can be traced back to 17th century
Considering the great prospect of fractional order system and Cohen-Grossberg neural networks, we aim to study the finite-time synchronization (FTS) of memristor-based fractional order Cohen-Grossberg neural networks (MFCGNNs)
Motivated by the above discussions, in this paper, we focus our attention on the FTS of MFCGNNs

Summary

Introduction

Fractional order calculus can be traced back to 17th century. The theory of fractional calculus was once considered as a purely theoretical field of mathematics and developed very slow. It has become a great research focus due to the fact that many fractional order models play a key role in the modeling of practical applications, such as market dynamics, viscoelastic systems, diffusion waves, electrical circuits, signal processing, system identification and so on [1]–[3]. The study of dynamics of fractional order differential systems has attracted the interest of many scholars and many interesting and important results have been reported including the control technology for factional order differential systems [4]–[8].

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