Title :
Chaos Synchronization of Coupled FitzHugh-Nagumo Neurons Via Adaptive Sliding Mode Control
Author :
Che, Yan-Qiu ; Cui, Shi-Gang ; Wang, Jiang ; Bin Deng ; Wei, Xi-Le
Author_Institution :
Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
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.
Keywords :
Lyapunov methods; adaptive control; approximation theory; biocontrol; chaos; neurocontrollers; nonlinear dynamical systems; radial basis function networks; stability; synchronisation; uncertain systems; variable structure systems; Lyapunov stability theory; adaptive neural network sliding mode controller; approximate error; chaos synchronization; closed error system; error dynamical system; external disturbances; gap junction coupled FitzHugh-Nagumo neurons; ionic channel noise; radial basis function; uncertain nonlinear part; Artificial neural networks; Chaos; Electrical stimulation; Junctions; Neurons; Robustness; Synchronization; Chaos synchronization; FitzHugh-Nagumo model; RBF neural networks; Sliding mode control;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on
Conference_Location :
Shangshai
Print_ISBN :
978-1-4244-9010-3
DOI :
10.1109/ICMTMA.2011.173
