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Design and Implementation of a Noise Tolerant Polynomial Nonlinear ARX Model using the Averaging Wavelet Method

by Ehsan Khadem Olama, Hooshang Jazayeri-Rad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 1
Year of Publication: 2011
Authors: Ehsan Khadem Olama, Hooshang Jazayeri-Rad
10.5120/4362-6012

Ehsan Khadem Olama, Hooshang Jazayeri-Rad . Design and Implementation of a Noise Tolerant Polynomial Nonlinear ARX Model using the Averaging Wavelet Method. International Journal of Computer Applications. 35, 1 ( December 2011), 1-5. DOI=10.5120/4362-6012

@article{ 10.5120/4362-6012,
author = { Ehsan Khadem Olama, Hooshang Jazayeri-Rad },
title = { Design and Implementation of a Noise Tolerant Polynomial Nonlinear ARX Model using the Averaging Wavelet Method },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 1 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number1/4362-6012/ },
doi = { 10.5120/4362-6012 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:20:57.574799+05:30
%A Ehsan Khadem Olama
%A Hooshang Jazayeri-Rad
%T Design and Implementation of a Noise Tolerant Polynomial Nonlinear ARX Model using the Averaging Wavelet Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 1
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a new nonlinear wavelet identification structure is proposed for high noise resistive soft sensors. This method uses proposed Polynomial Nonlinear Auto Regressive Exogenous Model, which can be solved with linear Gaussian Least Square Method, alongside the Averaging Wavelet Method (AWM) filter. AWM uses the approximation spaces for analyzing the signals and reduce the noise by a mean filtering over sub-resolutions. Conventional wavelet modeling methods use the detail spaces of the decomposed signal for signal modeling. The application results show that this method can be more accurate in high level noisy environments than the conventional wavelet modeling methods can tolerate.

References
  1. Radunovic, D. P., 2009, "Wavelets from Math to Practice," Springer, Berlin.
  2. Aadaleesan, P., and Saha, P., 2008, "Nonlinear System Identification Using Laguerre Wavelet Models," Chemical Product and Process Modeling, 3(2).
  3. Ebadat, A., Noroozi, N., Safavi, A. A., and Mousavi, S. H., 2010, "Modeling and control of nonlinear systems using novel fuzzy wavelet networks: The modeling approach " Decision and Control (CDC2010) IEEE, Atlanta, GA.
  4. Olama, E. K., 2011, "Averaging Wavelet Method: MATLAB codes," http://ekoshv.persiangig.com/MATLABCODES/AWM/AWM.zip.
  5. E.K. Olama, and Valiloo, S., 2011, "A fast wavelet denoising method," Computer Research and Development (ICCRD)Shanghai pp. 492 - 494.
  6. F.Walnut, D., 2002, An Introduction to Wavelet Analysis, Birkhauster.
  7. Nelles, O., 2001, "Nonlinear System Identification: From classical approches to neural networks and fuzzy models," Springer, Berlin, pp. 569-574.
Index Terms

Computer Science
Information Sciences

Keywords

Averaging Wavelet Method NARX Noise tolerant modeling

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