General decay anti-synchronization of coupled delayed memristive neural networks

Aobo Li; Yanli Huang; Yan Liu; Zewei Zheng

doi:10.1109/YAC59482.2023.10401634

General decay anti-synchronization of coupled delayed memristive neural networks

Aobo Li
, Yanli Huang^*
, Yan Liu
, Zewei Zheng

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Tiangong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, the network models of coupled delayed memristive neural networks (CDMNNs) without and with delayed coupling are studied respectively. Firstly, according to the definitions of ψ-type stability and ψ -type function, the notion of general decay anti-synchronization (GDAS) is introduced. Secondly, by constructing Lyapunov functional, designing controller and using inequality techniques, the corresponding sufficient conditions that reaching GDAS for CDMNNs with and without delayed coupling are deduced. Thirdly, the validity of the results is proved by proper numerical simulation.

Original language	English
Title of host publication	Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	142-147
Number of pages	6
ISBN (Electronic)	9798350303636
DOIs	https://doi.org/10.1109/YAC59482.2023.10401634
State	Published - 2023
Event	38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023 - Hefei, China Duration: 27 Aug 2023 → 29 Aug 2023

Publication series

Name	Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

Conference

Conference	38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023
Country/Territory	China
City	Hefei
Period	27/08/23 → 29/08/23

Keywords

coupled delayed memristive neural networks
delayed coupling
general decay anti-synchronization

Access to Document

10.1109/YAC59482.2023.10401634

Cite this

Li, A., Huang, Y., Liu, Y., & Zheng, Z. (2023). General decay anti-synchronization of coupled delayed memristive neural networks. In Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023 (pp. 142-147). (Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/YAC59482.2023.10401634

Li, Aobo ; Huang, Yanli ; Liu, Yan et al. / General decay anti-synchronization of coupled delayed memristive neural networks. Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 142-147 (Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023).

@inproceedings{0b13d17e01594edd95168c437f634b0c,

title = "General decay anti-synchronization of coupled delayed memristive neural networks",

abstract = "In this paper, the network models of coupled delayed memristive neural networks (CDMNNs) without and with delayed coupling are studied respectively. Firstly, according to the definitions of ψ-type stability and ψ -type function, the notion of general decay anti-synchronization (GDAS) is introduced. Secondly, by constructing Lyapunov functional, designing controller and using inequality techniques, the corresponding sufficient conditions that reaching GDAS for CDMNNs with and without delayed coupling are deduced. Thirdly, the validity of the results is proved by proper numerical simulation.",

keywords = "coupled delayed memristive neural networks, delayed coupling, general decay anti-synchronization",

author = "Aobo Li and Yanli Huang and Yan Liu and Zewei Zheng",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023 ; Conference date: 27-08-2023 Through 29-08-2023",

year = "2023",

doi = "10.1109/YAC59482.2023.10401634",

language = "英语",

series = "Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "142--147",

booktitle = "Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023",

address = "美国",

}

Li, A, Huang, Y, Liu, Y & Zheng, Z 2023, General decay anti-synchronization of coupled delayed memristive neural networks. in Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023. Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 142-147, 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023, Hefei, China, 27/08/23. https://doi.org/10.1109/YAC59482.2023.10401634

General decay anti-synchronization of coupled delayed memristive neural networks. / Li, Aobo; Huang, Yanli; Liu, Yan et al.
Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 142-147 (Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - General decay anti-synchronization of coupled delayed memristive neural networks

AU - Li, Aobo

AU - Huang, Yanli

AU - Liu, Yan

AU - Zheng, Zewei

PY - 2023

Y1 - 2023

N2 - In this paper, the network models of coupled delayed memristive neural networks (CDMNNs) without and with delayed coupling are studied respectively. Firstly, according to the definitions of ψ-type stability and ψ -type function, the notion of general decay anti-synchronization (GDAS) is introduced. Secondly, by constructing Lyapunov functional, designing controller and using inequality techniques, the corresponding sufficient conditions that reaching GDAS for CDMNNs with and without delayed coupling are deduced. Thirdly, the validity of the results is proved by proper numerical simulation.

AB - In this paper, the network models of coupled delayed memristive neural networks (CDMNNs) without and with delayed coupling are studied respectively. Firstly, according to the definitions of ψ-type stability and ψ -type function, the notion of general decay anti-synchronization (GDAS) is introduced. Secondly, by constructing Lyapunov functional, designing controller and using inequality techniques, the corresponding sufficient conditions that reaching GDAS for CDMNNs with and without delayed coupling are deduced. Thirdly, the validity of the results is proved by proper numerical simulation.

KW - coupled delayed memristive neural networks

KW - delayed coupling

KW - general decay anti-synchronization

UR - https://www.scopus.com/pages/publications/85185591018

U2 - 10.1109/YAC59482.2023.10401634

DO - 10.1109/YAC59482.2023.10401634

M3 - 会议稿件

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T3 - Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

SP - 142

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BT - Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

Y2 - 27 August 2023 through 29 August 2023

ER -

Li A, Huang Y, Liu Y, Zheng Z. General decay anti-synchronization of coupled delayed memristive neural networks. In Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 142-147. (Proceedings - 2023 38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023). doi: 10.1109/YAC59482.2023.10401634

General decay anti-synchronization of coupled delayed memristive neural networks

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