Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons

This paper mainly tends to investigate finite-time stability and stabilization of impulsive stochastic delayed neural networks with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs). Firstly, by constructing the proper Lyapunov-Krasovskii functional and employing t...

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Main Authors:	Tao Chen, Shiguo Peng, Yinghan Hong, Guizhen Mai
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Access
Subjects:	Finite-time stability and stabilization impulsive stochastic neural networks average impulsive interval time-varying delay ROUs RONs
Online Access:	https://ieeexplore.ieee.org/document/9089015/

id	doaj-6b27f307832f45ecaf42b1c8fc29bc56
record_format	Article
spelling	doaj-6b27f307832f45ecaf42b1c8fc29bc562021-03-30T03:14:12ZengIEEEIEEE Access2169-35362020-01-018871338714110.1109/ACCESS.2020.29926869089015Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and RonsTao Chen0https://orcid.org/0000-0001-5320-9930Shiguo Peng1Yinghan Hong2https://orcid.org/0000-0002-6322-6563Guizhen Mai3https://orcid.org/0000-0001-9433-7883School of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou, ChinaThis paper mainly tends to investigate finite-time stability and stabilization of impulsive stochastic delayed neural networks with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs). Firstly, by constructing the proper Lyapunov-Krasovskii functional and employing the average impulsive interval method, several novel criteria for ensuring the finite-time stability of impulsive stochastic delayed neural networks are obtained by means of linear matrix inequalities (LMIs). Then, some conditions about the state feedback controller are derived to ensure the finite-time stabilization of impulsive stochastic delayed neural networks with ROUs and RONs. Finally, numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed results.https://ieeexplore.ieee.org/document/9089015/Finite-time stability and stabilizationimpulsive stochastic neural networksaverage impulsive intervaltime-varying delayROUsRONs
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Tao Chen Shiguo Peng Yinghan Hong Guizhen Mai
spellingShingle	Tao Chen Shiguo Peng Yinghan Hong Guizhen Mai Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons IEEE Access Finite-time stability and stabilization impulsive stochastic neural networks average impulsive interval time-varying delay ROUs RONs
author_facet	Tao Chen Shiguo Peng Yinghan Hong Guizhen Mai
author_sort	Tao Chen
title	Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons
title_short	Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons
title_full	Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons
title_fullStr	Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons
title_full_unstemmed	Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons
title_sort	finite-time stability and stabilization of impulsive stochastic delayed neural networks with rous and rons
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2020-01-01
description	This paper mainly tends to investigate finite-time stability and stabilization of impulsive stochastic delayed neural networks with randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs). Firstly, by constructing the proper Lyapunov-Krasovskii functional and employing the average impulsive interval method, several novel criteria for ensuring the finite-time stability of impulsive stochastic delayed neural networks are obtained by means of linear matrix inequalities (LMIs). Then, some conditions about the state feedback controller are derived to ensure the finite-time stabilization of impulsive stochastic delayed neural networks with ROUs and RONs. Finally, numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed results.
topic	Finite-time stability and stabilization impulsive stochastic neural networks average impulsive interval time-varying delay ROUs RONs
url	https://ieeexplore.ieee.org/document/9089015/
work_keys_str_mv	AT taochen finitetimestabilityandstabilizationofimpulsivestochasticdelayedneuralnetworkswithrousandrons AT shiguopeng finitetimestabilityandstabilizationofimpulsivestochasticdelayedneuralnetworkswithrousandrons AT yinghanhong finitetimestabilityandstabilizationofimpulsivestochasticdelayedneuralnetworkswithrousandrons AT guizhenmai finitetimestabilityandstabilizationofimpulsivestochasticdelayedneuralnetworkswithrousandrons
_version_	1724183869266067456

Finite-Time Stability and Stabilization of Impulsive Stochastic Delayed Neural Networks With Rous and Rons

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