Design and application of soft sensor using ensemble methods

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

Volumn , Issue , 2011, Pages

  Soares, Symone ^a   Araújo, Rui ^a   Sousa, Pedro ^a,b   Souza, Francisco ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b Parque Empresarial de Eiras   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL POLICY; DESIGN AND APPLICATION; ENSEMBLE METHODS; HARDWARE SENSORS; HIGHLY-CORRELATED; LABORATORY MEASUREMENTS; MONITORING AND CONTROL; NOISE INJECTION; PAPER PULP; PRODUCT QUALITY; PULP PROCESS; RAW MATERIAL CONSUMPTION; SMALL DATA SET; SOFT SENSORS;

CHEMICAL OXYGEN DEMAND; FACTORY AUTOMATION; SENSORS;

PROCESS CONTROL;

EID: 80655146441     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ETFA.2011.6059061     Document Type: Conference Paper

References (28)

1. S. Akaho. Regularization learning of neural networks for generalization. In Algorithmic Learning Theory, volume 743 of Lecture Notes in Computer Science, pages 99-110. Springer-Verlag, Berlin/Heidelberg, 1993.
2. R. Andonie. Extreme data mining: Inference from small datasets. International Journal of Computers Communications & Control, V(3):280-291, 2010.
3. G. Bebis and M. Georgwpoulos. Improving generalization by using genetic algorithms to determine the neural network size. In Southcon/95. Conference Record, pages 392-397, March 7-9 1995.
4. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, August 1996. (Pubitemid 126724382)
5. G. Brown. Diversity in Neural Network Ensembles. PhD thesis, University of Birmingham, Birmingham, UK, 2004.
6. G. Brown, J. Wyatt, R. Harris, and X. Yao. Diversity creation methods: A survey and categorisation. Information Fusion, 6(1):5-20, 2005.
7. M. Champagne, M. Amazouz, and R. Platon. The application of soft sensors in the pulp and paper and cement manufacturing sectors for process and energy performance improvement: Opportunity analysis and technology assessment. Technical Report CETC 2005-100 (TR), Canmet Energy Technology Centre, Varennes, Canada, June 2005.
8. A. Chandra, H. Chen, and X. Yao. Trade-off between diversity and accuracy in ensemble generation. In Y. Jin, editor, Multi-Objective Machine Learning, volume 16 of Studies in Computational Intelligence, pages 429-464. Springer Berlin / Heidelberg, 2006.
9. T. G. Dietterich. Ensemble methods in machine learning. In J. Kittler and F. Roli, editors, Multiple Classifier Systems, Proceedings of the First International Workshop on (MCS 2000), volume 1857 of Lecture Notes in Computer Science, pages 1-15. Springer-Verlag, Berlin/Heidelberg, 2000.
10. B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall/CRC, London, UK, 1993.
11. L. Fortuna, S. Graziani, A. Rizzo, and M. G. Xibilia. Soft Sensors for Monitoring and Control of Industrial Processes. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2007.
12. L. Fortuna, S. Graziani, and M. G. Xibilia. Comparison of soft-sensor design methods for industrial plants using small data sets. IEEE Transactions on Instrumentation and Measurement, 58(8):2444-2451, August 2009.
13. Y. Freund and R. Schapire. A short introduction to boosting. Japonese Society for Artificial Intelligence, 14(5):771-780, 1999.
14. H. J. Galicia, Q. P. He, and J. Wang. A reduced order soft sensor approach and its application to a continuous digester. Journal of Process Control, In Press, Corrected Proof, 2011.
15. Y. Grandvalet, S. Canu, and S. Boucheron. Noise injection: Theoretical prospects. Neural Computation, 9(5):1093-1108, July 1997. (Pubitemid 127462793)
16. S. HanWoo, C. O. Jeon, Y.-S. Yun, H. Choi, C.-S. Lee, and D. S. Lee. On-line estimation of key process variables based on kernel partial least squares in an industrial cokes wastewater treatment plant. Journal of Hazardous Materials, 161(1):538-544, January 2009.
17. R. M. Z. Y. Jiang, L. L. Pesce, and K. Drukker. Noise injection for training artificial neural networks: A comparison with weight decay and early stopping. The International Journal of Medical Physics Research and Practice, 36(10):4810-4818, September 2009.
18. P. Kadlec and B. Gabrys. Nature-inspired adaptive architecture for soft sensor modelling. In Third European Symposium on Nature-inspired Smart Information Systems, November 2007.
19. P. Kadlec, B. Gabrys, and S. Strandt. Data-driven soft sensors in the process industry. Computers & Chemical Engineering, 33(4):795-814, 2009.
20. A. Krogh and J. A. Hertz. A simple weight decay can improve generalization. In J. Moody, S. Hanson, and R. Lippmann, editors, Advances in Neural Information Processing Systems, volume 4, pages 950-957. Morgan Kauffmann Publishers, San Mateo, CA, USA, 1995.
21. G. Liu, D. Zhou, H. Xu, and C. Mei. Model optimization of svm for a fermentation soft sensor. Expert Systems With Applications, 37(4):2708-2713, April 2010.
22. D. Opitz and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Re- search, 11:169-198, 1999. (Pubitemid 129628763)
23. R. Polikar. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3):21-45, 2006.
24. Y. Raviv and N. Intrator. Bootstrapping with noise: An effective regularization technique. Connection Science, 8(3):355-372, 1996.
25. N. Ueda and R. Nakano. Generalization error of ensemble estimators. In Neural Networks, 1996., IEEE International Conference on, volume 1, pages 90-95, June 1996.
26. D. Wang, J. Liu, and R. Srinivasan. Data-driven soft sensor approach for quality prediction in a refining process. IEEE Transactions on Industrial Informatics, 6(1):11-17, February 2010.
27. Z. Yuan and H. Liu. Soft sensor for apparent degree of calcination in nsp cement production line. In Proc. 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), volume 1, pages 473-478, February 2010.
28. G. P. Zhang. A neural network ensemble method with jittered training data for time series forecasting. Information Sciences, 177(23):5329-5346, December 2007.