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GPR model with signal preprocessing and bias update for dynamic processes modeling

Ni, W, Wang, K, Chen, T, Ng, WJ and Tan, SK (2012) GPR model with signal preprocessing and bias update for dynamic processes modeling Control Engineering Practice, 20 (12). pp. 1281-1292.

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Abstract

This paper introduces a Gaussian process regression (GPR) model which could adapt to both linear and nonlinear systems automatically without prior introduction of kernel functions. The applications of GPR model for two industrial examples are presented. The first example addresses a biological anaerobic system in a wastewater treatment plant and the second models a nonlinear dynamic process of propylene polymerization. Special emphasis is placed on signal preprocessing methods including the Savitzky-Golay and Kalman filters. Applications of these filters are shown to enhance the performance of the GPR model, and facilitate bias update leading to reduction of the offset between the predicted and measured values. © 2012 Elsevier Ltd.

Item Type: Article
Authors :
NameEmailORCID
Ni, WUNSPECIFIEDUNSPECIFIED
Wang, KUNSPECIFIEDUNSPECIFIED
Chen, TUNSPECIFIEDUNSPECIFIED
Ng, WJUNSPECIFIEDUNSPECIFIED
Tan, SKUNSPECIFIEDUNSPECIFIED
Date : December 2012
Identification Number : 10.1016/j.conengprac.2012.07.003
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:47
Last Modified : 31 Oct 2017 15:04
URI: http://epubs.surrey.ac.uk/id/eprint/771516

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