On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays

This article studies the stability problem of a class of stochastic neutral-type inertial delay neural networks. By introducing appropriate variable transformations, the second-order differential system is transformed into a first-order differential system. Using homeomorphism mapping, standard stoc...

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Main Authors: Bingxian Wang, Honghui Yin, Bo Du
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/15/9/1746
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author Bingxian Wang
Honghui Yin
Bo Du
author_facet Bingxian Wang
Honghui Yin
Bo Du
author_sort Bingxian Wang
collection DOAJ
description This article studies the stability problem of a class of stochastic neutral-type inertial delay neural networks. By introducing appropriate variable transformations, the second-order differential system is transformed into a first-order differential system. Using homeomorphism mapping, standard stochastic analyzing technology, the Lyapunov functional method and the properties of a neutral operator, we establish new sufficient criteria for the unique existence and stochastically globally asymptotic stability of equilibrium points. An example is also provided, to show the validity of the established results. From our results, we find that, under appropriate conditions, random disturbances have no significant impact on the existence, stability, and symmetry of network systems.
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spelling doaj.art-40751745341c47d2bdb9dcfe29bbcb922023-11-19T13:12:04ZengMDPI AGSymmetry2073-89942023-09-01159174610.3390/sym15091746On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed DelaysBingxian Wang0Honghui Yin1Bo Du2School of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, ChinaSchool of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, ChinaSchool of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, ChinaThis article studies the stability problem of a class of stochastic neutral-type inertial delay neural networks. By introducing appropriate variable transformations, the second-order differential system is transformed into a first-order differential system. Using homeomorphism mapping, standard stochastic analyzing technology, the Lyapunov functional method and the properties of a neutral operator, we establish new sufficient criteria for the unique existence and stochastically globally asymptotic stability of equilibrium points. An example is also provided, to show the validity of the established results. From our results, we find that, under appropriate conditions, random disturbances have no significant impact on the existence, stability, and symmetry of network systems.https://www.mdpi.com/2073-8994/15/9/1746neutral-type inertial neural networksstochasticstabilitydelays
spellingShingle Bingxian Wang
Honghui Yin
Bo Du
On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays
Symmetry
neutral-type inertial neural networks
stochastic
stability
delays
title On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays
title_full On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays
title_fullStr On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays
title_full_unstemmed On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays
title_short On Asymptotic Properties of Stochastic Neutral-Type Inertial Neural Networks with Mixed Delays
title_sort on asymptotic properties of stochastic neutral type inertial neural networks with mixed delays
topic neutral-type inertial neural networks
stochastic
stability
delays
url https://www.mdpi.com/2073-8994/15/9/1746
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