Chaos synchronization of coupled neurons via adaptive sliding mode control

被引：26
作者：
Che, Yan-Qiu [1 ]
Wang, Jiang [2 ]
Cui, Shi-Gang [1 ]
Deng, Bin [2 ]
Wei, Xi-Le [2 ]
Chan, Wai-Lok [3 ]
Tsang, Kai-Ming [3 ]
机构：
[1] Tianjin Univ Technol & Educ, Tianjin Key Lab Informat Sensing & Intelligent Co, Sch Automat & Elect Engn, Tianjin, Peoples R China
[2] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China
[3] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
来源：
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2011年 / 12卷 / 06期
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
Chaos synchronization; FitzHugh-Nagumo (FHN) model; RBF neural networks; Sliding mode control; HODGKIN-HUXLEY NEURONS; EXTERNAL ELECTRICAL-STIMULATION; UNIDIRECTIONAL SYNCHRONIZATION; VISUAL-CORTEX; SYSTEMS; RESPONSES; FEEDBACK; BRAIN; CELLS;
D O I：
10.1016/j.nonrwa.2011.05.020
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, an adaptive neural network (NN) sliding mode controller (SMC) is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (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. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：3199 / 3206
页数：8
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