Abstract
In this paper, the stabilization problems are discussed for a class of stochastic nonlinear systems with Markov jump based on neural network. By using Lyapunov function and backstepping, a neural network control strategy is designed to guarantee such system is asymptotically stable in probability. The single hidden layer feed-forward neural network (SLFNN) is employed to compensate the unknown nonlinear parts in system. The parameters of the SLFNN are designed by extreme learning machine (ELM), in which the hidden layer node parameters are generated stochastically. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.
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