A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model

Authors

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

Abstract

Abstract: For some time-varying industrial processes with non-Gaussian properties, the model established by the general soft-sensing method is difficult to meet the accuracy requirement. To solve the above problems effectively, a JITL soft sensor modeling method (BGMM) is proposed based on Bayesian Gaussian Mixture Model. For the given training sample set, the number of components of the Gaussian mixture model is optimized by Bayesian Information Criterion (BIC); For new test samples, Gaussian Process Regression (GPR) model is established by using the BGMM similarity criterion for the training samples to find out the most similar set;The model is used to predict the test samples. The effectiveness of the proposed method is verified by modeling and simulating the concentration of butane at the butane tower bottom .

Recommended Citation

Cheng, Qi and Xiong, Weili (2019) "A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model," Journal of System Simulation: Vol. 31: Iss. 8, Article 9.
DOI: 10.16182/j.issn1004731x.joss.17-0285
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss8/9

First Page

1555

Revised Date

2017-07-28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0285

Last Page

1561

CLC

TP273

Recommended Citation

Qi Cheng, Xiong Weili. A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model[J]. Journal of System Simulation, 2019, 31(8): 1555-1561.

DOI

10.16182/j.issn1004731x.joss.17-0285