Event-Triggered Synchronization for Memristor-Based Neural Networks

Hao Zhang; Tong Wang; Qingshuang Zeng; Jianbin Qiu

doi:10.1109/ICSSE50014.2020.9219300

Event-Triggered Synchronization for Memristor-Based Neural Networks

School of Astronautics

Harbin Institute of Technology

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

Abstract

This paper studies the synchronization problem of memristor-based neural networks with networked environment, in which the network bandwidth and computational resources are limited. An event-triggered state feedback control strategy is proposed. By taking the proposed triggering mechanism into account, some criteria are developed such that memristor-based neural networks achieve synchronization with limited network bandwidth and computational resource. Different from the previous related works, continuous event detectors are adopted to determine when to broadcast synchronized error information to control input, and the Zeno phenomena are considered. Finally, a numerical simulation example is given to verify the effectiveness of the proposed event-triggered control strategy.

Original language	English
Title of host publication	2020 International Conference on System Science and Engineering, ICSSE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728159607
DOIs	https://doi.org/10.1109/ICSSE50014.2020.9219300
State	Published - Aug 2020
Event	2020 International Conference on System Science and Engineering, ICSSE 2020 - Kagawa, Japan Duration: 31 Aug 2020 → 3 Sep 2020

Publication series

Name	2020 International Conference on System Science and Engineering, ICSSE 2020

Conference

Conference	2020 International Conference on System Science and Engineering, ICSSE 2020
Country/Territory	Japan
City	Kagawa
Period	31/08/20 → 3/09/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Synchronization
event-triggered scheme
memristor-based neural networks

Access to Document

10.1109/ICSSE50014.2020.9219300

Cite this

Zhang, H., Wang, T., Zeng, Q., & Qiu, J. (2020). Event-Triggered Synchronization for Memristor-Based Neural Networks. In 2020 International Conference on System Science and Engineering, ICSSE 2020 Article 9219300 (2020 International Conference on System Science and Engineering, ICSSE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSSE50014.2020.9219300

@inproceedings{fba54f18c2f94a3181e94da9ecfebb53,

title = "Event-Triggered Synchronization for Memristor-Based Neural Networks",

abstract = "This paper studies the synchronization problem of memristor-based neural networks with networked environment, in which the network bandwidth and computational resources are limited. An event-triggered state feedback control strategy is proposed. By taking the proposed triggering mechanism into account, some criteria are developed such that memristor-based neural networks achieve synchronization with limited network bandwidth and computational resource. Different from the previous related works, continuous event detectors are adopted to determine when to broadcast synchronized error information to control input, and the Zeno phenomena are considered. Finally, a numerical simulation example is given to verify the effectiveness of the proposed event-triggered control strategy.",

keywords = "Synchronization, event-triggered scheme, memristor-based neural networks",

author = "Hao Zhang and Tong Wang and Qingshuang Zeng and Jianbin Qiu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on System Science and Engineering, ICSSE 2020 ; Conference date: 31-08-2020 Through 03-09-2020",

year = "2020",

month = aug,

doi = "10.1109/ICSSE50014.2020.9219300",

language = "英语",

series = "2020 International Conference on System Science and Engineering, ICSSE 2020",

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

booktitle = "2020 International Conference on System Science and Engineering, ICSSE 2020",

address = "美国",

}

Zhang, H, Wang, T , Zeng, Q & Qiu, J 2020, Event-Triggered Synchronization for Memristor-Based Neural Networks. in 2020 International Conference on System Science and Engineering, ICSSE 2020., 9219300, 2020 International Conference on System Science and Engineering, ICSSE 2020, Institute of Electrical and Electronics Engineers Inc., 2020 International Conference on System Science and Engineering, ICSSE 2020, Kagawa, Japan, 31/08/20. https://doi.org/10.1109/ICSSE50014.2020.9219300

Event-Triggered Synchronization for Memristor-Based Neural Networks. / Zhang, Hao; Wang, Tong ; Zeng, Qingshuang et al.
2020 International Conference on System Science and Engineering, ICSSE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9219300 (2020 International Conference on System Science and Engineering, ICSSE 2020).

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

TY - GEN

T1 - Event-Triggered Synchronization for Memristor-Based Neural Networks

AU - Zhang, Hao

AU - Wang, Tong

AU - Zeng, Qingshuang

AU - Qiu, Jianbin

PY - 2020/8

Y1 - 2020/8

N2 - This paper studies the synchronization problem of memristor-based neural networks with networked environment, in which the network bandwidth and computational resources are limited. An event-triggered state feedback control strategy is proposed. By taking the proposed triggering mechanism into account, some criteria are developed such that memristor-based neural networks achieve synchronization with limited network bandwidth and computational resource. Different from the previous related works, continuous event detectors are adopted to determine when to broadcast synchronized error information to control input, and the Zeno phenomena are considered. Finally, a numerical simulation example is given to verify the effectiveness of the proposed event-triggered control strategy.

AB - This paper studies the synchronization problem of memristor-based neural networks with networked environment, in which the network bandwidth and computational resources are limited. An event-triggered state feedback control strategy is proposed. By taking the proposed triggering mechanism into account, some criteria are developed such that memristor-based neural networks achieve synchronization with limited network bandwidth and computational resource. Different from the previous related works, continuous event detectors are adopted to determine when to broadcast synchronized error information to control input, and the Zeno phenomena are considered. Finally, a numerical simulation example is given to verify the effectiveness of the proposed event-triggered control strategy.

KW - Synchronization

KW - event-triggered scheme

KW - memristor-based neural networks

UR - https://www.scopus.com/pages/publications/85095615053

U2 - 10.1109/ICSSE50014.2020.9219300

DO - 10.1109/ICSSE50014.2020.9219300

M3 - 会议稿件

AN - SCOPUS:85095615053

T3 - 2020 International Conference on System Science and Engineering, ICSSE 2020

BT - 2020 International Conference on System Science and Engineering, ICSSE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on System Science and Engineering, ICSSE 2020

Y2 - 31 August 2020 through 3 September 2020

ER -

Event-Triggered Synchronization for Memristor-Based Neural Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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