Neural network H-infinity synchronization control for time delay chaotic neuronal systems

Yanqiu Che; Bei Liu; Huiyan Li; Yingmei Qin; Chunxiao Han

doi:10.1109/CCDC.2016.7531994

Neural network H-infinity synchronization control for time delay chaotic neuronal systems

Yanqiu Che, Bei Liu, Huiyan Li, Yingmei Qin, Chunxiao Han

Department of Neurosurgery

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This paper proposes a hybrid synchronization scheme for chaotic systems with input time delay and uncertainty. In the proposed framework, radial basis function (RBF) neural networks (NNs) are constructed to approximate the unknown smooth nonlinear functions of the synchronization error system. The time delay part is dealt with an adaptive controller and the effect of approximate errors, uncertainties and disturbances are reduced to a H∞ norm constraint. By Lyapunov stability theorem, the closed-loop of the controlled synchronization error system is proved to be stable around zero. Thus the synchronization of chaotic systems is obtained. A simulation example with Hindmarsh-Rose neuronal systems is presented to demonstrate the validity of the proposed control method.

Original language	English (US)
Title of host publication	Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5575-5580
Number of pages	6
ISBN (Electronic)	9781467397148
DOIs	https://doi.org/10.1109/CCDC.2016.7531994
State	Published - Aug 3 2016
Event	28th Chinese Control and Decision Conference, CCDC 2016 - Yinchuan, China Duration: May 28 2016 → May 30 2016

Publication series

Name	Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016

Other

Other	28th Chinese Control and Decision Conference, CCDC 2016
Country/Territory	China
City	Yinchuan
Period	5/28/16 → 5/30/16

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Control and Optimization
Statistics, Probability and Uncertainty
Artificial Intelligence
Decision Sciences (miscellaneous)

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10.1109/CCDC.2016.7531994

Cite this

Che, Y., Liu, B., Li, H., Qin, Y., & Han, C. (2016). Neural network H-infinity synchronization control for time delay chaotic neuronal systems. In Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016 (pp. 5575-5580). Article 7531994 (Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC.2016.7531994

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title = "Neural network H-infinity synchronization control for time delay chaotic neuronal systems",

abstract = "This paper proposes a hybrid synchronization scheme for chaotic systems with input time delay and uncertainty. In the proposed framework, radial basis function (RBF) neural networks (NNs) are constructed to approximate the unknown smooth nonlinear functions of the synchronization error system. The time delay part is dealt with an adaptive controller and the effect of approximate errors, uncertainties and disturbances are reduced to a H∞ norm constraint. By Lyapunov stability theorem, the closed-loop of the controlled synchronization error system is proved to be stable around zero. Thus the synchronization of chaotic systems is obtained. A simulation example with Hindmarsh-Rose neuronal systems is presented to demonstrate the validity of the proposed control method.",

author = "Yanqiu Che and Bei Liu and Huiyan Li and Yingmei Qin and Chunxiao Han",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 28th Chinese Control and Decision Conference, CCDC 2016 ; Conference date: 28-05-2016 Through 30-05-2016",

year = "2016",

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doi = "10.1109/CCDC.2016.7531994",

language = "English (US)",

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Che, Y, Liu, B, Li, H, Qin, Y & Han, C 2016, Neural network H-infinity synchronization control for time delay chaotic neuronal systems. in Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016., 7531994, Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 5575-5580, 28th Chinese Control and Decision Conference, CCDC 2016, Yinchuan, China, 5/28/16. https://doi.org/10.1109/CCDC.2016.7531994

Neural network H-infinity synchronization control for time delay chaotic neuronal systems. / Che, Yanqiu; Liu, Bei; Li, Huiyan et al.
Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 5575-5580 7531994 (Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Neural network H-infinity synchronization control for time delay chaotic neuronal systems

AU - Che, Yanqiu

AU - Liu, Bei

AU - Li, Huiyan

AU - Qin, Yingmei

AU - Han, Chunxiao

PY - 2016/8/3

Y1 - 2016/8/3

N2 - This paper proposes a hybrid synchronization scheme for chaotic systems with input time delay and uncertainty. In the proposed framework, radial basis function (RBF) neural networks (NNs) are constructed to approximate the unknown smooth nonlinear functions of the synchronization error system. The time delay part is dealt with an adaptive controller and the effect of approximate errors, uncertainties and disturbances are reduced to a H∞ norm constraint. By Lyapunov stability theorem, the closed-loop of the controlled synchronization error system is proved to be stable around zero. Thus the synchronization of chaotic systems is obtained. A simulation example with Hindmarsh-Rose neuronal systems is presented to demonstrate the validity of the proposed control method.

AB - This paper proposes a hybrid synchronization scheme for chaotic systems with input time delay and uncertainty. In the proposed framework, radial basis function (RBF) neural networks (NNs) are constructed to approximate the unknown smooth nonlinear functions of the synchronization error system. The time delay part is dealt with an adaptive controller and the effect of approximate errors, uncertainties and disturbances are reduced to a H∞ norm constraint. By Lyapunov stability theorem, the closed-loop of the controlled synchronization error system is proved to be stable around zero. Thus the synchronization of chaotic systems is obtained. A simulation example with Hindmarsh-Rose neuronal systems is presented to demonstrate the validity of the proposed control method.

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M3 - Conference contribution

AN - SCOPUS:84983775598

T3 - Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016

SP - 5575

EP - 5580

BT - Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th Chinese Control and Decision Conference, CCDC 2016

Y2 - 28 May 2016 through 30 May 2016

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Che Y, Liu B, Li H, Qin Y, Han C. Neural network H-infinity synchronization control for time delay chaotic neuronal systems. In Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 5575-5580. 7531994. (Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016). doi: 10.1109/CCDC.2016.7531994

Neural network H-infinity synchronization control for time delay chaotic neuronal systems

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