Ten lectures on wavelets
The comb signal and its Fourier transform
Signal Processing - Special section on digital signal processing for multimedia communications and services
Fast Reconstruction Methods for Bandlimited Functions from Periodic Nonuniform Sampling
SIAM Journal on Numerical Analysis
EURASIP Journal on Applied Signal Processing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
An improved Akaike information criterion for state-space model selection
Computational Statistics & Data Analysis
Sampling theorem and irregular sampling theorem for multiwavelet subspaces
IEEE Transactions on Signal Processing
Sampling theorems for uniform and periodic nonuniform MIMO sampling of multiband signals
IEEE Transactions on Signal Processing
Spectral analysis of randomly sampled signals: suppression of aliasing and sampler jitter
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Prediction and identification using wavelet-based recurrent fuzzy neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Multiwavelet neural network and its approximation properties
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Wavelet neural networks: A practical guide
Neural Networks
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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.