Reduced Noise Effect in Nonlinear Model Estimation Using Multiscale Representation

Abstract EN

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.