American Institute of Mathematical Sciences

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September  2007, 6(3): 873-897. doi: 10.3934/cpaa.2007.6.873

Wavelet approach to numerical differentiation of noisy functions

Jianzhong Wang 1,

1. 

Department of Mathematics and Statis, Sam Houston State University, 1901 Avenue J., P.O. Box 2206, Huntsville, TX 77341-2206, United States

Received  April 2006 Revised  April 2007 Published  June 2007

We apply wavelet transform in the study of numerical differentiation for the functions which are infected by noise. Because of the presence of noise, the observed noisy function is not differentiable. In order to estimate the derivatives of the target function from its observation, a pretreatment of the observation is necessary. The paper introduces differential approximation wavelets (DA-wavelets) so that the DA-wavelet transforms of the observed function approximate the derivatives of the target function. The paper also shows that the derivatives of compactly supported splines lead to a certain type of DA-wavelet transforms, which are difference formulas for computing derivatives. The relation between difference formulas and splines enables us to construct various difference formulas via splines and to estimate the computing errors of difference formulas in the spline framework.
Keywords: noisy data, data analysis., numerical differentiation, Wavelets, spline.
Mathematics Subject Classification: Primary: 42C40, 65D25; Secondary: 41A15, 65D07, 93E1.
Citation: Jianzhong Wang. Wavelet approach to numerical differentiation of noisy functions. Communications on Pure & Applied Analysis, 2007, 6 (3) : 873-897. doi: 10.3934/cpaa.2007.6.873
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