Iterative algorithm of wavelet network learning from nonuniform data

The learning algorithm based on multiresolution analysis (LAMA) is a powerful tool for wavelet networks. It has many advantages over other algorithms, but it seldom does well in the learning of nonuniform data. A new algorithm is proposed to solve this problem, which develops from the learning algorithm based on sampling theory (LAST). From the good concentration of wavelet energy, we discuss the approximation capacity of wavelet network in the local domain when the training data are not dense enough. From this discussion, the new algorithm is realized by the iterative application of LAST. The corresponding theorems based on the sampling theory are also proposed to prove the rationality of new algorithm. In the simulation, we compare the performance of new algorithm with that of LAMA and LAST. The results show that our new algorithm has as many advantages as LAMA and LAST, does better in the learning of nonuniform data and has high approximation accuracy.