Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100842
Title: GPR model with signal preprocessing and bias update for dynamic processes modeling
Authors: Ni, Wangdong
Wang, Ke
Chen, Tao
Ng, Wun Jern
Tan, Soon Keat
Issue Date: 2012
Source: Ni, W., Wang, K., Chen, T., Ng, W. J., & Tan, S. K. (2012). GPR model with signal preprocessing and bias update for dynamic processes modeling. Control Engineering Practice, 20(12), 1281-1292.
Series/Report no.: Control engineering practice
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.
URI: https://hdl.handle.net/10356/100842
http://hdl.handle.net/10220/10816
ISSN: 0967-0661
DOI: 10.1016/j.conengprac.2012.07.003
Rights: © 2012 Elsevier Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles
NEWRI Journal Articles
SCBE Journal Articles

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