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

›› 2009, Vol. 17 ›› Issue (1): 95-99.

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

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

LI Xiuliang, SU Hongye, CHU Jian

National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Received:2007-08-12 Revised:2008-10-14 Online:2009-01-28 Published:2009-01-28
Supported by:
Supported by the National High Technology Research and Development Program of China (2006AA040309);National BasicResearch Program of China (2007CB714000)

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

李修亮, 苏宏业, 褚健

National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

通讯作者: SU Hongye, E-mail: hysu@iipc.zju.edu.cn
基金资助:
Supported by the National High Technology Research and Development Program of China (2006AA040309);National BasicResearch Program of China (2007CB714000)

Abstract

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, 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.

Key words: multiple model, soft sensor, affinity propagation, Gaussian process, Bayesian committee machine

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.

关键词: multiple model, soft sensor, affinity propagation, Gaussian process, Bayesian committee machine

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.

李修亮, 苏宏业, 褚健. Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine[J]. , 2009, 17(1): 95-99.

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

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

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