Table of Contents Author Guidelines Submit a Manuscript
Modelling and Simulation in Engineering
Volume 2010 (2010), Article ID 217305, 8 pages
http://dx.doi.org/10.1155/2010/217305
Research Article

Reduced Noise Effect in Nonlinear Model Estimation Using Multiscale Representation

1Chemical Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar
2Electrical and Computer Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

Received 24 November 2009; Revised 25 March 2010; Accepted 13 May 2010

Academic Editor: Borut Zupancic

Copyright © 2010 Mohamed N. Nounou and Hazem N. Nounou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nonlinear process models are widely used in various applications. In the absence of fundamental models, it is usually relied on empirical models, which are estimated from measurements of the process variables. Unfortunately, measured data are usually corrupted with measurement noise that degrades the accuracy of the estimated models. Multiscale wavelet-based representation of data has been shown to be a powerful data analysis and feature extraction tool. In this paper, these characteristics of multiscale representation are utilized to improve the estimation accuracy of the linear-in-the-parameters nonlinear model by developing a multiscale nonlinear (MSNL) modeling algorithm. The main idea in this MSNL modeling algorithm is to decompose the data at multiple scales, construct multiple nonlinear models at multiple scales, and then select among all scales the model which best describes the process. The main advantage of the developed algorithm is that it integrates modeling and feature extraction to improve the robustness of the estimated model to the presence of measurement noise in the data. This advantage of MSNL modeling is demonstrated using a nonlinear reactor model.