Adaptive soft sensor for online prediction based on moving window gaussian process regression

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 2, Issue , 2012, Pages 428-433

  Grbić, Ratko ^a   Slišković, Dražen ^a   Kadlec, Petr ^b  

^a JOSIP JURAJ STROSSMAYER UNIVERSITY OF OSIJEK   (Croatia)

^b EVONIK INDUSTRIES AG   (Germany)

Author keywords

adaptive soft sensor; Gaussian Process Regression; Mutual Information; online prediction; process modeling

Indexed keywords

ADAPTIVE SOFT-SENSOR; GAUSSIAN PROCESS REGRESSION; MUTUAL INFORMATIONS; ONLINE PREDICTION; PROCESS MODELING;

FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LEARNING SYSTEMS; PROCESS CONTROL; PROCESS MONITORING; REGRESSION ANALYSIS;

SENSORS;

EID: 84873603969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.160     Document Type: Conference Paper

References (32)

1. T. J. McAvoy, "Intelligent 'Control' Applications in the process industries," Annual Reviews in Control, vol. 26, no. 1, pp. 75-86, 2002.
2. L. Fortuna, S. Graziani, A. Rizzo, and M. G. Xibilia, Soft sensors for monitoring and control of industrial processes. Springer Verlag, 2007.
3. P. Kadlec, B. Gabrys, and S. Strandt, "Data-driven Soft Sensors in the process industry," Computers & Chemical Engineering, vol. 33, no. 4, pp. 795-814, Apr. 2009.
4. B. Lin, B. Recke, J. K. H. Knudsen, and S. B. Jorgensen, "A systematic approach for soft sensor development," Computers & Chemical Engineering, vol. 31, no. 5-6, pp. 419-425, 2007. (Pubitemid 46282062)
5. D. Sliškovi, R. Grbi-, and E. K. Nyarko, "Data preprocessing in data based process modeling," in Intelligent Control Systems and Signal Processing, 2009, vol. 2, no. 1, pp. 559-565.
6. H. Martens and T. Naes, Multivariate calibration. John Wiley & Sons Inc, 1992.
7. L. Fortuna, S. Graziani, and M. Xibilia, "Soft sensors for product quality monitoring in debutanizer distillation columns," Control Engineering Practice, vol. 13, no. 4, pp. 499-508, Apr. 2005. (Pubitemid 40032159)
8. M. Shakil, M. Elshafei, M. A. Habib, and F. A. Maleki, "Soft sensor for NOx and O2 using dynamic neural networks," Computers & Electrical Engineering, vol. 35, no. 4, pp. 578-586, 2009.
9. W. Yan, H. Shao, and X. Wang, "Soft sensing modeling based on support vector machine and Bayesian model selection," Computers & Chemical Engineering, vol. 28, no. 8, pp. 1489-1498, 2004. (Pubitemid 38609097)
10. S. J. Qin, "Neural networks for intelligent sensors and control-practical issues and some solutions," Neural Systems for Control, pp. 213-234, 1997.
11. J. Liu, "On-line soft sensor for polyethylene process with multiple production grades," Control Engineering Practice, vol. 15, no. 7, pp. 769-778, Jul. 2007. (Pubitemid 46577935)
12. D. Sliškovi, R. Grbi, and Ž. Hocenski, "Methods for Plant Data-Based Process Modeling in Soft-Sensor Development," Automatika, vol. 52, no. 4, pp. 306-318, 2011.
13. Z. Ge and Z. Song, "Nonlinear Soft Sensor Development Based on Relevance Vector Machine," Industrial & Engineering Chemistry Research, vol. 49, no. 18, pp. 8685-8693, Sep. 2010.
14. Z. Ge, F. Gao, and Z. Song, "Mixture probabilistic PCR model for soft sensing of multimode processes," Chemometrics and Intelligent Laboratory Systems, vol. 105, no. 1, pp. 91-105, Jan. 2011.
15. C. E. Rasmussen and C. K. I. Williams, Gaussian processes in machine learning, vol. 14, no. 2. MIT Press, 2006.
16. Z. Ge, T. Chen, and Z. Song, "Quality prediction for polypropylene production process based on CLGPR model," Control Engineering Practice, vol. 19, no. 5, pp. 423-432, May 2011.
17. F. di Sciascio and A. N. Amicarelli, "Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression," Computers & Chemical Engineering, vol. 32, no. 12, pp. 3264-3273, Dec. 2008.
18. P. Kadlec, R. Grbi, and B. Gabrys, "Review of adaptation mechanisms for data-driven soft sensors," Computers & Chemical Engineering, vol. 35, no. 1, pp. 1-24, Jan. 2011.
19. S. Haykin, Neural networks: a comprehensive foundation. Prentice hall, 1999.
20. F. Rossi, A. Lendasse, D. François, V. Wertz, and M. Verleysen, "Mutual information for the selection of relevant variables in spectrometric nonlinear modelling," Chemometrics and Intelligent Laboratory Systems, vol. 80, no. 2, pp. 215-226, Feb. 2006. (Pubitemid 43259500)
21. T. M. Cover and J. A. Thomas, Elements of information theory. New York: John Wiley and Sons Inc., 1991.
22. A. Kraskov, H. Stögbauer, and P. Grassberger, "Estimating mutual information," Physical Review E, vol. 69, no. 6, 2004.
23. J. C. Jeng, "Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms," Journal of the Taiwan Institute of Chemical Engineers, vol. 41, no. 4, pp. 475-481, Jul. 2010.
24. B. S. Dayal and J. F. MacGregor, "Recursive exponentially weighted PLS and its applications to adaptive control and prediction," Journal of Process Control, vol. 7, no. 3, pp. 169-179, 1997. (Pubitemid 127372162)
25. P. Kadlec and B. Gabrys, "Soft sensor based on adaptive local learning," Advances in Neuro-Information Processing, pp. 1172-1179, 2009.
26. S. Vijayakumar, A. D'Souza, and S. Schaal, "Incremental online learning in high dimensions.," Neural computation, vol. 17, no. 12, pp. 2602-34, Dec. 2005. (Pubitemid 41505186)
27. K. Fujiwara, M. Kano, S. Hasebe, and A. Takinami, "Softsensor development using correlation-based just-in-time modeling," AIChE Journal, vol. 55, no. 7, pp. 1754-1765, 2009.
28. S. V. Vaerenbergh and J. Via, "A sliding-window kernel RLS algorithm and its application to nonlinear channel identification," in IEEE Conference on Acoustics, Speech and Signal Processsing, 2006.
29. W. Liu, J. C. Principe, and S. Haykin, Kernel Adaptive Filtering: A Comprehensive Introduction. John Wiley and Sons Inc., 2010.
30. Y. Engel, S. Mannor, and R. Meir, "The kernel recursive least-squares algorithm," Signal Processing, IEEE Transactions on, vol. 52, no. 8, pp. 2275-2285, Aug. 2004.
31. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," Machine learning, vol. 101, 1996.
32. A. Tsymbal. The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15, Department of Computer Science Trinity College, Dublin, 2004.