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Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control. / Yao, Wei; Wang, Chunhua; Sun, Yichuang; Zhou, Chao; Lin, Hairong .

In: Neurocomputing, Vol. 404, 03.09.2020, p. 367-380.

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@article{c7e44a3206f4463f90ed5056974f510b,
title = "Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control",
abstract = "This paper investigates the synchronization problem of inertial memristive neural networks (IMNNs) with time-varying delays via event-triggered control (ETC) scheme and state feedback controller for the first time. First, two types of state feedback controllers are designed; the first type of controller is added to the transformational first-order system, and the second type of controller is added to the original second-order system. Next, based on each feedback controller, static event-triggered control (SETC) condition and dynamic event-triggered control (DETC) condition are presented to significantly reduce the update times of controller and decrease the computing cost. Then, some sufficient conditions are given such that synchronization of IMNNs with time-varying delays can be achieved under ETC schemes. Finally, a numerical simulation and some data analyses are given to verify the validity of the proposed results.",
keywords = "Event-triggered control, Inertial memristive neural networks, State feedback controllers, Synchronization",
author = "Wei Yao and Chunhua Wang and Yichuang Sun and Chao Zhou and Hairong Lin",
note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61971185, the Major Research Project of the National Natural Science Foundation of China under Grant 91964108 and the Open Fund Project of Key Laboratory in Hunan Universities under Grant 18K010. Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",
year = "2020",
month = sep,
day = "3",
doi = "10.1016/j.neucom.2020.04.099",
language = "English",
volume = "404",
pages = "367--380",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control

AU - Yao, Wei

AU - Wang, Chunhua

AU - Sun, Yichuang

AU - Zhou, Chao

AU - Lin, Hairong

N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61971185, the Major Research Project of the National Natural Science Foundation of China under Grant 91964108 and the Open Fund Project of Key Laboratory in Hunan Universities under Grant 18K010. Publisher Copyright: © 2020 Elsevier B.V.

PY - 2020/9/3

Y1 - 2020/9/3

N2 - This paper investigates the synchronization problem of inertial memristive neural networks (IMNNs) with time-varying delays via event-triggered control (ETC) scheme and state feedback controller for the first time. First, two types of state feedback controllers are designed; the first type of controller is added to the transformational first-order system, and the second type of controller is added to the original second-order system. Next, based on each feedback controller, static event-triggered control (SETC) condition and dynamic event-triggered control (DETC) condition are presented to significantly reduce the update times of controller and decrease the computing cost. Then, some sufficient conditions are given such that synchronization of IMNNs with time-varying delays can be achieved under ETC schemes. Finally, a numerical simulation and some data analyses are given to verify the validity of the proposed results.

AB - This paper investigates the synchronization problem of inertial memristive neural networks (IMNNs) with time-varying delays via event-triggered control (ETC) scheme and state feedback controller for the first time. First, two types of state feedback controllers are designed; the first type of controller is added to the transformational first-order system, and the second type of controller is added to the original second-order system. Next, based on each feedback controller, static event-triggered control (SETC) condition and dynamic event-triggered control (DETC) condition are presented to significantly reduce the update times of controller and decrease the computing cost. Then, some sufficient conditions are given such that synchronization of IMNNs with time-varying delays can be achieved under ETC schemes. Finally, a numerical simulation and some data analyses are given to verify the validity of the proposed results.

KW - Event-triggered control

KW - Inertial memristive neural networks

KW - State feedback controllers

KW - Synchronization

UR - http://www.scopus.com/inward/record.url?scp=85085237770&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2020.04.099

DO - 10.1016/j.neucom.2020.04.099

M3 - Article

VL - 404

SP - 367

EP - 380

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -