Curve Fitting of Irregularly Sampled Data by Multiwavelets Neural Networks
نویسندگان
چکیده
Unshifted and shifted multiscaling functions are used as mathematical models for curve fitting of irregularly sampled data. This pre-processing procedure combined with multiwavelet neural networks for data-adaptive curve fitting is shown to perform well in the case of high resolution. In the case of low resolution it is more accurate than numerical integration and cheaper than matrix inversion. In the case of large data, it saves memory as compared with the conjugate gradient method for the same computational cost.
منابع مشابه
Multiwavelet neural network preprocessing of irregularly sampled data
Multiwavelets are briefly reviewed and preprocessing and postprocessing for such wavelets are introduced. Least squares curve fitting of irregularly sampled data is achieved by means of unshifted and shifted multiscaling functions. This preprocessing procedure combined with multiwavelet neural networks for data-adaptive curve fitting is shown to perform well in the case of high resolution. In t...
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