Synchronization of fractional-order memristive recurrent neural networks via aperiodically intermittent control

Shuai Zhang; Yongqing Yang; Xin Sui; Yanna Zhang; Shuai Zhang; Yongqing Yang; Xin Sui; Yanna Zhang

doi:10.3934/mbe.2022545

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 11: 11717-11734. doi: 10.3934/mbe.2022545

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Synchronization of fractional-order memristive recurrent neural networks via aperiodically intermittent control

1.
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2.
Engineering Lab of Intelligence Business & Internet of Things, Henan Province, Xinxiang 453007, China
3.
College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China
4.
School of Science, Jiangnan University, Wuxi 214122, China
5.
School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China

Academic Editor: Xiaodi Li

Received: 28 June 2022 Revised: 29 July 2022 Accepted: 07 August 2022 Published: 16 August 2022

In this paper, synchronization of fractional-order memristive recurrent neural networks via aperiodically intermittent control is investigated. Considering the special properties of memristor neural network, differential inclusion theory is introduced. Similar to the aperiodically strategy of integer order, aperiodically intermittent control strategy of fractional order is proposed. Under the framework of Fillipov's solution, based on the intermittent strategy of fractional order systems and the properties Mittag-Leffler, sufficient criteria of aperiodically intermittent strategy are obtained by constructing appropriate Lyapunov functional. Some comparisons are given to demonstrate the advantages of aperiodically strategy. A simulation example is given to illustrate the derived conclusions.

Keywords:

Citation: Shuai Zhang, Yongqing Yang, Xin Sui, Yanna Zhang. Synchronization of fractional-order memristive recurrent neural networks via aperiodically intermittent control[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11717-11734. doi: 10.3934/mbe.2022545

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Abstract

A correction on

"Data augmentation based semi-supervised method to improve COVID-19 CT classification" [Mathematical Biosciences and Engineering 20(4) (2023) 6838–6852]

By Xiangtao Chen, Yuting Bai, Peng Wang and Jiawei Luo

DOI: 10.3934/mbe.2023294

Following publication, the authors have identified inappropriate references (References [3–5, 7, 10, 17]) included in the article ^[1]. To ensure the accuracy of our published work, we have decided to remove these references from the manuscript. The changes have no material impact on the conclusions of the article.

This correction has been approved by the Editor-in-Chief. We appreciate the support of the editorial office in ensuring the integrity of the published work.

We apologize for any inconvenience caused.

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