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Being difficult to determine hidden unitspsilas number and unsuitable to select central position in radial basis function (RBF) layer, particle swarm optimization and resource allocation (RAN) were proposed for training RBF neural networks. First, determine unitspsilas number in RBF layer using RAN. Then, optimize RBF parameters such as central position, width and weights based on PSO. The simulation results show that the new method has better approximation ability, the shorter time and the higher precision.