Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

李修亮; 苏宏业; 褚健

doi:10.1016/S1004-9541(09)60039-2

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Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

Updated：2026-01-06

- Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine
- Chinese Journal of Chemical Engineering Vol. 17, Issue 1, Pages: 95-99(2009)
- Affiliations：
  
  National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(09)60039-2
  CLC：
- Published：2009
- Accepted：
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LI Xiuliang, SU Hongye, CHU Jian. Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine[J]. , 2009, 17(1): 95-99.
DOI：

LI Xiuliang, SU Hongye, CHU Jian. Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine[J]. , 2009, 17(1): 95-99. DOI： 10.1016/S1004-9541(09)60039-2.

摘要

Presented is a multiple model soft sensing method based on Affinity Propagation(AP)

Gaussian process(GP) and Bayesian committee machine(BCM).AP clustering arithmetic is used to cluster training samples according to their operating points.Then

the sub-models are estimated by Gaussian Process Regression(GPR).Finally

in order to get a global probabilistic prediction

Bayesian committee machine is used to combine the outputs of the sub-estimators.The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators.Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

Abstract

Presented is a multiple model soft sensing method based on Affinity Propagation(AP)

Gaussian process(GP) and Bayesian committee machine(BCM).AP clustering arithmetic is used to cluster training samples according to their operating points.Then

the sub-models are estimated by Gaussian Process Regression(GPR).Finally

in order to get a global probabilistic prediction

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