Chaos synchronization of coupled neurons via adaptive sliding mode control

Yan Qiu Che, Jiang Wang, Shi Gang Cui, Bin Deng, Xi Le Wei, Wai Lok Chan, Kai Ming Tsang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


In this paper, an adaptive neural network (NN) sliding mode controller (SMC) is proposed to realize the chaos synchronization of two gap junction coupled FitzHughNagumo (FHN) neurons under external electrical stimulation. The controller consists of a radial basis function (RBF) NN and an SMC. After the RBFNN approximating the uncertain nonlinear part of the error dynamical system, the SMC realizes the desired control property regardless of the existence of the approximation errors and external disturbances. The weights of the NN 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 is obtained by the proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.
Original languageEnglish
Pages (from-to)3199-3206
Number of pages8
JournalNonlinear Analysis: Real World Applications
Issue number6
Publication statusPublished - 1 Dec 2011


  • Chaos synchronization
  • FitzHughNagumo (FHN) model
  • RBF neural networks
  • Sliding mode control

ASJC Scopus subject areas

  • Analysis
  • General Engineering
  • Economics, Econometrics and Finance(all)
  • Computational Mathematics
  • Applied Mathematics


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