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

›› 2009, Vol. 17 ›› Issue (1): 95-99.

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

收稿日期:2007-08-12 修回日期:2008-10-14 出版日期:2009-01-28 发布日期:2009-01-28
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)

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)

摘要/Abstract

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

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

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.

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.

参考文献

1 Liu, J.L., “On-line soft sensor for polyethylene process with multiple production grades”, Control Engineering Practice, 15, 769-778 (2007).
2 Feng, R., Zhang, Y.Z., Song, C.L., Shao, H.H., “A multiple model approach to modeling based on fuzzy support vector machines”, Journal of Shanghai Jiaotong University, 8 (2), 137-141 (2003).
3 Zhang, Y., Li, N., Huang, D., “Soft sensor of polyester reaction based on multiple neural network model”, Journal of East China University of Science and Technology (Natural Science Edition), 31 (2), 208-211 (2005).
4 Gui, W.H., Li, Y.G., Yang, C.H., Chen, Z.S., “Distributed SVM based on improved clustering algorithm and its application”, Control and Decision, 19 (8), 852-856 (2004).
5 Du, W.L., Qian, F., Liu, M.D., Zhang, K., “4-CBA soft sensor based on fuzzy CMAC neural networks”, Chin. J. Chem. Eng., 13 (3),437-440 (2005).
6 Zhang, Y., Su, H.Y., Liu, R.L., Chu, J., “Fuzzy support vector regression model of 4-CBA concentration for industrial PTA oxidation process”, Chin. J. Chem. Eng., 13 (5), 642-648 (2005).
7 Wang, J.L., Yu, T., Jin, C.Y., “On-line estimation of biomass in fermentation process using support vector machine”, Chin. J. Chem. Eng., 14 (3), 383-388 (2006).
8 Frey, J., Dueck, D., “Clustering by passing messages between data points”, Science, 315 (5814), 972-976 (2007).
9 Rasmussen, C.E., Williams, C.K.I., Gaussian Process for Machine Learning, MIT Press, USA, 8-31(2006).
10 Likar, B., Kocijian, J., “Predictive control of a gas-liquid separation plant based on Gaussian process model”, Comp. Chem. Eng., 31,142-152 (2007).
11 Tresp, V., “A Bayesian committee machine”, Neural Computation,12, 2719-2741 (2000).
12 Tresp, V., “The generalization Bayesian committee machine”, In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, NY, USA,130-139 (2000).
13 King, M.J., “How to lose money with inferential properties”, Hydrocarbon Processing, 83 (10), 47-52 (2004).
14 Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G., Soft Sensors for Monitoring and Control of Industrial Processes, Springer Verlag, London (2006).
15 http://www.springer.com/engineering/robotics/book/978-1-84628-479-3
16 Schladt, M., Hu, B., “Soft sensors based on nonlinear steady-state data reconciliation in the process industry”, Chemical Engineering and Processing, 46, 1107-1115 (2007).

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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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