Ensemble independent component regression models and soft sensing application

Chemometrics and Intelligent Laboratory Systems

Volumn 130, Issue , 2014, Pages 115-122

  Ge, Zhiqiang ^a   Song, Zhihuan ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Ensemble learning model; Independent component regression; Soft sensor

Indexed keywords

POLYPROPYLENE;

ANALYTIC METHOD; ARTICLE; INDEPENDENT COMPONENT REGRESSION; INDUSTRY; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; ONLINE MONITORING; PARTIAL LEAST SQUARES REGRESSION; POLYMER PRODUCTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; REGRESSION ANALYSIS; SOFT SENSOR;

EID: 84887725182     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.09.009     Document Type: Article

References (31)

1. Kadlec P., Gabrys B., Strandt S. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 2009, 33:795-814.
2. Gustafsson M.G. Independent component analysis yields chemically interpretable latent variables in multivariate regression. J. Chem. Inf. Model. 2005, 45:1244-1255.
3. Hyvarinen A., Oja E. Independent component analysis: algorithms and applications. Neural Netw. 2000, 13:411-430.
4. Li R.F., Wang X.Z. Dimension reduction of process dynamic trends using independent component analysis. Comput. Chem. Eng. 2002, 26:467-473.
5. Ge Z.Q., Song Z.H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Ind. Eng. Chem. Res. 2007, 46:2054-2063.
6. Ge Z.Q., Song Z.H. Online monitoring of nonlinear multiple mode processes based on adaptive local model approach. Control. Eng. Pract. 2008, 16:1427-1437.
7. Tian X.M., Zhang X.L., Deng X.G., Chen S. Multiway kernel independent component analysis based on feature samples for batch process monitoring. Neurocomputing 2009, 72:1584-1596.
8. Ge Z.Q., Xie L., Kruger U., Song Z.H. Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions. AIChE J 2012, 58:2357-2372.
9. Javidi S., Took C.C., Mandic D.P. Fast independent component analysis algorithm for quaternion valued signals. IEEE Trans. Neural Netw. 2011, 22:1967-1978.
10. Ge Z.Q., Song Z.H., Gao F.R. Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res. 2013, 52:3543-3562.
11. Dai W.S., Wu J.Y., Lu C.J. Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes. Expert Syst. Appl. 2012, 39:4444-4452.
12. Chen J., Wang X.Z. A new approach to near-infrared spectral data analysis using independent component analysis. J. Chem. Inf. Comput. Sci. 2001, 41:992-1001.
13. Westad F. Independent component analysis and regression applied on sensory data. J. Chemometr. 2005, 19:171-179.
14. Shao X.G., Wang W., Hou Z.Y., Cai W.S. A new regression method based on independent component analysis. Talanta 2006, 69:676-680.
15. Ahn H., Choi E., Han I. Extracting underlying meaningful features and canceling noise using independent component analysis for direct marketing. Expert Syst. Appl. 2007, 33:181-191.
16. Kaneko H., Arakawa M., Funatsu K. Development of a new regression analysis method using independent component analysis. J. Chem. Inf. Model. 2008, 48:534-541.
17. Zhang Y.W., Zhang Y. Optimized independent components for parameter regression. Chemom. Intell. Lab. Syst. 2010, 104:214-222.
18. Shirali S., Hazra B., Narasimhan S., Pandey M.D. Independent component regression for predicting the responses of biaxial base-isolated buildings. Earthq. Eng. Struct. Dyn. 2010, 39:583-590.
19. Ai-Mbaideen A., Benaissa M. Determination of glucose concentration from NIR spectra using independent component regression. Chemom. Intell. Lab. Syst. 2011, 105:131-135.
20. Ai-Mbaideen A., Benaissa M. Coupling subband decomposition and independent component regression for quantitative NIR spectroscopy. Chemom. Intell. Lab. Syst. 2011, 108:112-122.
21. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
22. Ho T.K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20:832-844.
23. Tao D.C., Tang X.O., Li X.L., Wu X.D. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28:1088-1099.
24. Ge Z.Q., Zhang M.G., Song Z.H. Nonlinear process monitoring based on linear subspace and Bayesian inference. J. Process Control 2010, 20:676-688.
25. Cao D.S., Xu Q.S., Liang Y.Z. The boosting: a new idea of building models. Chemom. Intell. Lab. Syst. 2010, 100:1-11.
26. Wang G., Hao J.X., Ma J.A., Jiang H.B. A comparative assessment of ensemble learning for credit scoring. Expert Syst. Appl. 2011, 38:223-230.
27. Ge Z.Q., Song Z.H. Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring. Chemom. Intell. Lab. Syst. 2013, 123:1-8.
28. Ge Z.Q., Song Z.H. Bagging support vector data description model for batch process monitoring. J. Process Control 2013, 23:1090-1096.
29. Wang K., Chen T., Lau R. Bagging for robust non-linear multivariate calibration of spectroscopy. Chemom. Intell. Lab. Syst. 2011, 105:1-6.
30. Shi J., Liu X.G., Sun Y.X. Melt index prediction by neural networks based on independent component analysis and multi-scale analysis. Neurocomputing 2006, 70:280-287.
31. Kiparissides C., Verros G., MacGregor J.F. Mathematical modeling, optimization, and quality control of high pressure polymerization reactors. J. Macromol. Sci. Rev. Macromol. Chem. Phys. 1993, C33:437-527.