Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control

The drive-response synchronization of delayed neural networks with discontinuous activation functions is investigated via adaptive control. The synchronization of this paper means that the synchronization error approaches to zero for almost all time as time goes to infinity. The discontinuous activa...

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Main Authors: Xinsong Yang, Jinde Cao
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2013/147164
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spelling doaj-a7e829c6d7904b5fb16777c434ca306f2020-11-24T22:39:17ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2013-01-01201310.1155/2013/147164147164Synchronization of Discontinuous Neural Networks with Delays via Adaptive ControlXinsong Yang0Jinde Cao1Department of Mathematics, Chongqing Normal University, Chongqing 401331, ChinaDepartment of Mathematics, Southeast University, Nanjing 210096, ChinaThe drive-response synchronization of delayed neural networks with discontinuous activation functions is investigated via adaptive control. The synchronization of this paper means that the synchronization error approaches to zero for almost all time as time goes to infinity. The discontinuous activation functions are assumed to be monotone increasing which can be unbounded. Due to the mild condition on the discontinuous activations, adaptive control technique is utilized to control the response system. Under the framework of Filippov solution, by using Lyapunov function and chain rule of differential inclusion, rigorous proofs are given to show that adaptive control can realize complete synchronization of the considered model. The results of this paper are also applicable to continuous neural networks, since continuous function is a special case of discontinuous function. Numerical simulations verify the effectiveness of the theoretical results. Moreover, when there are parameter mismatches between drive and response neural networks with discontinuous activations, numerical example is also presented to demonstrate the complete synchronization by using discontinuous adaptive control.http://dx.doi.org/10.1155/2013/147164
collection DOAJ
language English
format Article
sources DOAJ
author Xinsong Yang
Jinde Cao
spellingShingle Xinsong Yang
Jinde Cao
Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control
Discrete Dynamics in Nature and Society
author_facet Xinsong Yang
Jinde Cao
author_sort Xinsong Yang
title Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control
title_short Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control
title_full Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control
title_fullStr Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control
title_full_unstemmed Synchronization of Discontinuous Neural Networks with Delays via Adaptive Control
title_sort synchronization of discontinuous neural networks with delays via adaptive control
publisher Hindawi Limited
series Discrete Dynamics in Nature and Society
issn 1026-0226
1607-887X
publishDate 2013-01-01
description The drive-response synchronization of delayed neural networks with discontinuous activation functions is investigated via adaptive control. The synchronization of this paper means that the synchronization error approaches to zero for almost all time as time goes to infinity. The discontinuous activation functions are assumed to be monotone increasing which can be unbounded. Due to the mild condition on the discontinuous activations, adaptive control technique is utilized to control the response system. Under the framework of Filippov solution, by using Lyapunov function and chain rule of differential inclusion, rigorous proofs are given to show that adaptive control can realize complete synchronization of the considered model. The results of this paper are also applicable to continuous neural networks, since continuous function is a special case of discontinuous function. Numerical simulations verify the effectiveness of the theoretical results. Moreover, when there are parameter mismatches between drive and response neural networks with discontinuous activations, numerical example is also presented to demonstrate the complete synchronization by using discontinuous adaptive control.
url http://dx.doi.org/10.1155/2013/147164
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AT jindecao synchronizationofdiscontinuousneuralnetworkswithdelaysviaadaptivecontrol
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