Chaos synchronization of coupled FitzHugh-Nagumo neurons via adaptive sliding mode control

Yanqiu Che, Shi Gang Cui, Jiang Wang, Bin Deng, Xi Le Wei

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

1 Scopus citations

Abstract

In this paper, an adaptive neural network (NN) sliding mode controller is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. The controller consists of two simple radial basis function (RBF) NNs which are used to approximate the desired sliding mode controller and the uncertain nonlinear part of the error dynamical system, respectively. The weights of these NNs are tuned online based on the sliding mode reaching law. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as approximate error, ionic channel noise and external disturbances. Chaos synchronization are obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011
Pages684-687
Number of pages4
DOIs
StatePublished - 2011
Event3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011 - Shanghai, China
Duration: Jan 6 2011Jan 7 2011

Publication series

NameProceedings - 3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011
Volume1

Other

Other3rd International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2011
Country/TerritoryChina
CityShanghai
Period1/6/111/7/11

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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