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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings of the 28th Chinese Control and Decision Conference, CCDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5575-5580
Number of pages6
ISBN (Electronic)9781467397148
DOIs
StatePublished - Aug 3 2016
Event28th Chinese Control and Decision Conference, CCDC 2016 - Yinchuan, China
Duration: May 28 2016May 30 2016

Publication series

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

Other

Other28th Chinese Control and Decision Conference, CCDC 2016
CountryChina
CityYinchuan
Period5/28/165/30/16

Fingerprint

Time Delay
Time delay
Synchronization
Infinity
Neural Networks
Neural networks
Chaotic systems
Chaotic System
Uncertainty
Input Delay
Lyapunov Theorem
Radial Basis Function Neural Network
Lyapunov Stability
Stability Theorem
Nonlinear Function
Smooth function
Closed-loop
Disturbance
Controller
Norm

All Science Journal Classification (ASJC) codes

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

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

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 proceedingConference contribution

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

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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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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). https://doi.org/10.1109/CCDC.2016.7531994