GPR Modeling Method Based on Kernel Slow Feature Analysis and Time Delay Estimation

Authors

Huilai Peng, 1. College of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; ;
Weili Xiong, 1. College of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; ;2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: A Gaussian process regression modeling method based on kernel slow feature analysis is proposed to deal with the time delay and nonlinear characteristics in industrial processes. The time delay in the industrial data is effectively extracted by fuzzy curve analysis and the optimal time-delay in the data is obtained. The model data is reconstructed based on the optimal delay. The method of kernel slow feature analysis is used to extract the nonlinear features of the reconstructed data. The Gaussian process regression model is established based on the extracted features. The effectiveness and performance of the method are verified by the simulation experiment of the soft measurement of butane concentration at the bottom of the de-butane tower.

Recommended Citation

Peng, Huilai and Xiong, Weili (2019) "GPR Modeling Method Based on Kernel Slow Feature Analysis and Time Delay Estimation," Journal of System Simulation: Vol. 31: Iss. 8, Article 10.
DOI: 10.16182/j.issn1004731x.joss.17-0389
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss8/10

First Page

1562

Revised Date

2017-11-04

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0389

Last Page

1571

CLC

TP273

Recommended Citation

Peng Huilai, Xiong Weili. GPR Modeling Method Based on Kernel Slow Feature Analysis and Time Delay Estimation[J]. Journal of System Simulation, 2019, 31(8): 1562-1571.

DOI

10.16182/j.issn1004731x.joss.17-0389