Wavelet neural network based on sampling theory has been found to have a good performance in function approximation. In this paper, this type of wavelet neural network is applied to modeling and control of a nonlinear dynamic system and some methods are employed to optimize the structure of wavelet neural network to prevent a large number of nodes. The direct inverse control technique is employed for investigating the ability of this network in control application. A variety of simulations are conducted for demonstrating the performance of the direct inverse control using wavelet neural network. The performance of this approach is compared with direct inverse control using multilayer perceptron neural network (MLP). Simulation results show that our proposed method reveals better stability and performance in reference tracking and control action.