Abstract:

To study the data transmission system of a ground station which is difficult to describe with a precise mathematical model, a radial basis function neural network modeling method with advanced gradient learning algorithm is proposed. Principal component analysis is used to determine the initial node number of hidden units. The method to compute the gradient information of network parameters is improved to accelerate convergence. The structure adjusting process is added to simplify the scale of networks. The algorithm is employed to the offline training of model parameters by sampling the input/output data of the system, and the realization details are also provided. Experiment results show that the model possesses a higher performance of generalization, and the advanced gradient learning algorithm has a better stability.