GPR model with signal preprocessing and bias update for dynamic processes modeling
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 anaer...
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sg-ntu-dr.10356-1008422020-03-07T11:40:20Z GPR model with signal preprocessing and bias update for dynamic processes modeling Ni, Wangdong Wang, Ke Chen, Tao Ng, Wun Jern Tan, Soon Keat School of Chemical and Biomedical Engineering School of Civil and Environmental Engineering Maritime Research Centre Nanyang Environment and Water Research Institute DHI-NTU Centre 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. 2013-06-28T00:44:42Z 2019-12-06T20:29:15Z 2013-06-28T00:44:42Z 2019-12-06T20:29:15Z 2012 2012 Journal Article 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. 0967-0661 https://hdl.handle.net/10356/100842 http://hdl.handle.net/10220/10816 10.1016/j.conengprac.2012.07.003 en Control engineering practice © 2012 Elsevier Ltd. |
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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. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Ni, Wangdong Wang, Ke Chen, Tao Ng, Wun Jern Tan, Soon Keat |
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Ni, Wangdong Wang, Ke Chen, Tao Ng, Wun Jern Tan, Soon Keat |
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Ni, Wangdong Wang, Ke Chen, Tao Ng, Wun Jern Tan, Soon Keat GPR model with signal preprocessing and bias update for dynamic processes modeling |
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Ni, Wangdong |
title |
GPR model with signal preprocessing and bias update for dynamic processes modeling |
title_short |
GPR model with signal preprocessing and bias update for dynamic processes modeling |
title_full |
GPR model with signal preprocessing and bias update for dynamic processes modeling |
title_fullStr |
GPR model with signal preprocessing and bias update for dynamic processes modeling |
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GPR model with signal preprocessing and bias update for dynamic processes modeling |
title_sort |
gpr model with signal preprocessing and bias update for dynamic processes modeling |
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2013 |
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https://hdl.handle.net/10356/100842 http://hdl.handle.net/10220/10816 |
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1681038084020371456 |