Bahri, Syamsul and Awalushaumi, Lailia and Susanto, Marliadi
The Approximation Of Nonlinear Function using Daubechies and Symlets Wavelets.
Proceedings of ICMIs.
(In Press)
Abstract
The phenomena and real world problems usually can be formulated as a representation of the problem of
function approximation, which is to estimate the value of a function f(x), based on the relationship or pattern
of the input-output data, that is sequences of ( ).
i i
y f x = In practice, some applications related to the
approximation of functions such as the problems of pattern classification, regression analysis, reconstruction
signals, and identification systems. The purpose of this research is to compare the performance of
Daubechies and Symlets wavelet types to estimate nonlinear functions. The characteristics of the
Daubechies and Symlets wavelet functions are smooth, regular, have a compact of the support and lengthy
of the filters, and an explicit the formula so it's good to handle smooth curves, reconstruct of signals, longer
filtering processes, easy and fast on computing process. The advantages of the Daubechies and Symlets
wavelet characteristics will be used as the basis for approximating non-linear functions. Numeracally,
based on the means square error (MSE) indicator, the results of this research provide an overview of the
accuracy of wavelet-based approximation by Daubechies and Symlets wavelets type for approximation of
the nonlinear function which is approximated is very significant.
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