Chaos synchronization of coupled neurons via H-infinity control with cooperative weights neural network

Yuliang Liu, Ruixue Li, Yanqiu Che, Chunxiao Han

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

1 Citation (Scopus)

Abstract

In this paper, an H-infinity control with a cooperative weights neural network is proposed to realize the synchronization of two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons. We first use a cooperative weights neural network to approximate the unknown nonlinear function. Then we employ the H-infinity control technique to attenuate the effects caused by unmodelled dynamics, disturbances and approximate errors. Finally, by Lyapunov method, the overall closed-loop system is shown to be stable and chaos synchronization is obtained. The control scheme is robust to the uncertainties such as unmodelled dynamics, ionic channel noises and external disturbances. The simulation results demonstrate the effectiveness of the proposed control method.

Original languageEnglish (US)
Title of host publicationAdvances in Future Computerand Control Systems
Pages369-374
Number of pages6
EditionVOL. 2
DOIs
StatePublished - May 18 2012
EventFuture Computer and Control Systems, FCCS 2012 - Changsha, China
Duration: Apr 21 2012Apr 22 2012

Publication series

NameAdvances in Intelligent and Soft Computing
NumberVOL. 2
Volume160 AISC
ISSN (Print)1867-5662

Other

OtherFuture Computer and Control Systems, FCCS 2012
CountryChina
CityChangsha
Period4/21/124/22/12

Fingerprint

Chaos theory
Neurons
Synchronization
Neural networks
Lyapunov methods
Closed loop systems

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Liu, Y., Li, R., Che, Y., & Han, C. (2012). Chaos synchronization of coupled neurons via H-infinity control with cooperative weights neural network. In Advances in Future Computerand Control Systems (VOL. 2 ed., pp. 369-374). (Advances in Intelligent and Soft Computing; Vol. 160 AISC, No. VOL. 2). https://doi.org/10.1007/978-3-642-29390-0_59
Liu, Yuliang ; Li, Ruixue ; Che, Yanqiu ; Han, Chunxiao. / Chaos synchronization of coupled neurons via H-infinity control with cooperative weights neural network. Advances in Future Computerand Control Systems. VOL. 2. ed. 2012. pp. 369-374 (Advances in Intelligent and Soft Computing; VOL. 2).
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Liu, Y, Li, R, Che, Y & Han, C 2012, Chaos synchronization of coupled neurons via H-infinity control with cooperative weights neural network. in Advances in Future Computerand Control Systems. VOL. 2 edn, Advances in Intelligent and Soft Computing, no. VOL. 2, vol. 160 AISC, pp. 369-374, Future Computer and Control Systems, FCCS 2012, Changsha, China, 4/21/12. https://doi.org/10.1007/978-3-642-29390-0_59

Chaos synchronization of coupled neurons via H-infinity control with cooperative weights neural network. / Liu, Yuliang; Li, Ruixue; Che, Yanqiu; Han, Chunxiao.

Advances in Future Computerand Control Systems. VOL. 2. ed. 2012. p. 369-374 (Advances in Intelligent and Soft Computing; Vol. 160 AISC, No. VOL. 2).

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

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AB - In this paper, an H-infinity control with a cooperative weights neural network is proposed to realize the synchronization of two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons. We first use a cooperative weights neural network to approximate the unknown nonlinear function. Then we employ the H-infinity control technique to attenuate the effects caused by unmodelled dynamics, disturbances and approximate errors. Finally, by Lyapunov method, the overall closed-loop system is shown to be stable and chaos synchronization is obtained. The control scheme is robust to the uncertainties such as unmodelled dynamics, ionic channel noises and external disturbances. The simulation results demonstrate the effectiveness of the proposed control method.

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Liu Y, Li R, Che Y, Han C. Chaos synchronization of coupled neurons via H-infinity control with cooperative weights neural network. In Advances in Future Computerand Control Systems. VOL. 2 ed. 2012. p. 369-374. (Advances in Intelligent and Soft Computing; VOL. 2). https://doi.org/10.1007/978-3-642-29390-0_59