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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...
In this paper, the state equation for the dynamics of quarter-car is established, and a stable robust sliding mode control law based on RBF neural network is presented for the vehicle slip ratio control. In addition, a moving sliding surface based on global sliding mode control is presented. Unlike the conventional sliding mode control, the moving sliding surface moves to the desired sliding surface...
The problem of output tracking control for a class of multi-input multi-output uncertain systems is considered. A novel adaptive robust controller is proposed, which incorporates a variable-structure radial basis function (RBF) network to approximate unknown system dynamics. The RBF network can determine its structure on-line dynamically, where radial basis functions are added or removed to ensure...
In this paper, a hybrid control strategy, variable universe adaptive fuzzy sliding mode control, is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. 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...
In this paper we propose a mathematical model to describe the dynamics of neurotransmitter's active, available and reprocessed states. A set of nonlinear differential equations is obtained from neurophysiological and biochemical reasoning. To solve a tracking control problem, we study cellular feedback mechanisms that regulate the amount of neurotransmitters at distinct states. The neurotransmitters...
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