Sampled-data synchronization control for chaotic neural networks subject to actuator saturation

In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchroni...

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Main Authors: Zeng, Hong-Bing, Teo, Kok Lay, He, Y., Xu, Honglei, Wang, Wei
Format: Journal Article
Published: Elsevier BV 2017
Online Access:http://hdl.handle.net/20.500.11937/52243
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author Zeng, Hong-Bing
Teo, Kok Lay
He, Y.
Xu, Honglei
Wang, Wei
author_facet Zeng, Hong-Bing
Teo, Kok Lay
He, Y.
Xu, Honglei
Wang, Wei
author_sort Zeng, Hong-Bing
building Curtin Institutional Repository
collection Online Access
description In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchronization error systems. By this condition, we design a sampled-data controller to regionally synchronize the drive neural networks and response neural networks subject to actuator saturation. Moreover, an optimization method is given to design the desired sampled-data controller such that the set of admissible initial conditions is maximized. A numerical example is given to demonstrate the effectiveness and merits of the proposed design technique.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:51:01Z
publishDate 2017
publisher Elsevier BV
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-522432017-09-13T15:48:50Z Sampled-data synchronization control for chaotic neural networks subject to actuator saturation Zeng, Hong-Bing Teo, Kok Lay He, Y. Xu, Honglei Wang, Wei In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchronization error systems. By this condition, we design a sampled-data controller to regionally synchronize the drive neural networks and response neural networks subject to actuator saturation. Moreover, an optimization method is given to design the desired sampled-data controller such that the set of admissible initial conditions is maximized. A numerical example is given to demonstrate the effectiveness and merits of the proposed design technique. 2017 Journal Article http://hdl.handle.net/20.500.11937/52243 10.1016/j.neucom.2017.02.063 Elsevier BV restricted
spellingShingle Zeng, Hong-Bing
Teo, Kok Lay
He, Y.
Xu, Honglei
Wang, Wei
Sampled-data synchronization control for chaotic neural networks subject to actuator saturation
title Sampled-data synchronization control for chaotic neural networks subject to actuator saturation
title_full Sampled-data synchronization control for chaotic neural networks subject to actuator saturation
title_fullStr Sampled-data synchronization control for chaotic neural networks subject to actuator saturation
title_full_unstemmed Sampled-data synchronization control for chaotic neural networks subject to actuator saturation
title_short Sampled-data synchronization control for chaotic neural networks subject to actuator saturation
title_sort sampled-data synchronization control for chaotic neural networks subject to actuator saturation
url http://hdl.handle.net/20.500.11937/52243