Universal approximation using radial-basis-function networks
Neural Computation
Regularization theory and neural networks architectures
Neural Computation
Image interpolation and resampling
Handbook of medical imaging
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Image interpolation for progressive transmission by using radial basis function networks
IEEE Transactions on Neural Networks
A Novel Structure for Radial Basis Function Networks--WRBF
Neural Processing Letters
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A fast method of computing radial basis function (RBF) coefficients for uniformly sampled data with the fast Fourier transform (FFT) is proposed. A periodic RBF network which is characterized by a set of periodic RBF coefficients is first introduced. The periodic RBF coefficients are then computed by using the FFT in O(N log N) computation time, where N is the number of observed data. The original RBF coefficients are approximately computed from the periodic RBF coefficients by using a sparse matrix transform in O(N) computation time. The approximation accuracy is theoretically investigated. The method for 1-D inputs is extended to 2-D by a successive computation of 1-D RBF coefficients.