Orthogonal Nonlinear Partial Least-Squares Regression

Industrial and Engineering Chemistry Research

Volumn 42, Issue 23, 2003, Pages 5836-5849

  Doymaz, Fuat ^a   Palazoglu, Ahmet ^a   Romagnoli, Jose A ^b  

^a University of California Davis   (United States)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; REGRESSION ANALYSIS;

PREDICTOR VARIABLES;

DRUG PRODUCTS;

DRUG;

MEASUREMENT METHOD;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CORRELATION ANALYSIS; DATA ANALYSIS; MEASUREMENT; NONLINEAR SYSTEM; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SHELF LIFE; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE;

EID: 0242330151     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie0109051     Document Type: Article

References (51)

1. Kresta, J. V.; Marlin, T. E.; MacGregor, J. F. Development of Inferential Process Models Using PLS. Comput. Chem. Eng. 1994, 18, 597.
2. Kesavan, P.; Lee, J. H.; Saucedo, V.; Krishnagopalan, G. A. PLS Based Monitoring and Control of Batch Digesters. In Proceedings of the 5th IFAC Symposium on Dynamics and Control of Process Systems; Georgakis, C., Ed.; IFAC: New York, 1998; p 773.
3. Warnes, M. R.; Glassey, J.; Montague, G. A.; Kara, B. On Data-Based Modelling Techniques for Fermentation Process. Process Biochem. 1996, 31, 147.
4. Trygg, J.; Wold, S. PLS Regression on Wavelet Compressed NIR Spectra. Chemom. Intell. Lab. Syst. 1998, 42, 209.
5. Geladi, P.; Hadjiiski, L.; Hopke, H. Multiple Regression for Environmental Data: Nonlinearities and Prediction Bias. Chemom. Intell. Lab. Syst. 1999, 47, 165.
6. Rawlings, J. O.; Pantula, S. G.; Dickey, D. A. Applied Regression Analysis: A Research Tool, 2nd ed.; Springer-Verlag: New York, 1998.
7. Box, G. E. P.; Cox, D. R. Analysis of Transformations. J. R. Stat. Soc. B 1964, 26, 211.
8. Rumelhart, D.; Hinton, G. E.; Williams, R. J. Learning internal representations by error propagations. In Parallel Distributed Processing; MIT Press: Cambridge, 1996; p 318.
9. Caruana, R.; Lawrence, S.; Giles, C. L. Overfitting in Neural Networks: Back-Propagation, Conjugate Gradient, and Early Stopping. In Advances in Neural Information Processing Systems, NIPS 13; Leen, T., Dietterich, T. G., Tresp, V., Eds.; MIT Press: Cambridge, MA, 2001; p 402.
10. Bhat, N. V.; McAvoy, T. J. Determining Model Structure for Neural Models by Network Pruning, Comput. Chem. Eng. 1992, 16, 271.
11. Massy, W. F. Principal Components Regression in Exploratory Statistical Research. JASA 1965, 60, 234.
12. Belsley, D. A.; Kuh, E.; Welsch, R. E. Applied Regression Diagnostics: Identifying Influential Data and Sources of Collinearity; Wiley: New York, 1980.
13. Wold, S.; Kettaneh-Wold, N.; Skagerberg, B. Nonlinear PLS Modeling. Chemom. Intell. Lab. Syst. 1989, 7, 53.
14. Höskuldson, A. Quadradic PLS Regression. J. Chemom. 1992, 6, 307.
15. Baffi, G.; Martin, E. B.; Morris, A. J. Nonlinear Projection to Latent Structures Revisited (The Quadratic PLS Algorithm). Comput. Chem. Eng. 1999, 23, 395.
16. Wold, S. Nonlinear Partial Least Squares Modelling: II. Spline Inner Relation. Chemom. Intell. Lab. Syst. 1992, 14, 71.
17. Qin, S. J.; MeAvoy, T. J. Nonlinear PLS Modeling Using Neural Networks. Comput. Chem. Eng. 1992, 16, 379.
18. Holcomb, T. R.; Morari, M. PLS/Neural Networks. Comput. Chem. Eng. 1992, 16, 393.
19. Baffi, G.; Martin, E. B.; Morris, A. J. Nonlinear Projection to Latent Structures Revisited (The Neural Network PLS Algorithm). Comput. Chem. Eng. 1999, 23, 1293.
20. Wilson, D. J. H.; Irwin, G. W.; Lightbody, G. Neural Networks and Multivariate SPC. In Proceedings of the IEE Colloquium on Fault Diagnosis in Process Systems; Digest No. 97/ 174; Patton, R. J., Ed.; IEE: London, 1997; pp 5/1-5/5.
21. Box, G. E. P.; Tidwell, W. G. Transformation of the independent variables. Technometrics 1962, 4, 531.
22. Qin, S. J. A Statistical Perspective of Neural Networks for Process Modeling and Control. In Proceedings of the International Symposium on Intelligent Control; IEEE Press: Piscataway, NJ, 1993; p 559.
23. Malthouse, E. C.; Tamhane, A. C.; Mah, R. S. H. Nonlinear partial least squares. Comput. Chem. Eng. 1997, 21, 875.
24. Wold, H. Nonlinear Estimation by iterative least squares procedures. In Research Papers in Statistics; Davis, F., Ed.; Wiley: New York, 1966; p 411.
25. Höskuldson, A. Prediction Methods in Science and Technology; Thor Publishing: Copenhagen, Denmark, 1996; Vol. 1 Basic Theory.
26. Geladi, P.; Kowalski, B. R. Partial Least Squares Regression: A Tutorial. Anal. Chim. Acta 1986, 185, 1-17.
27. Geladi, P.; Kowalski, B. R. An example of 2-block predictive partial least-squares regression with simulated data. Anal. Chim. Acta 1986, 185, 19.
28. Kramer, M. A. Nonlinear Principal Component Analysis Using Autoassociative Neural Networks. AIChE J. 1991, 37, 233.
29. Frank, I. NNPPSS: Neural Networks Based on PCR and PLS Components Nonlinearized by Smoothers and Splines. Presented at InCINC'94, the first International Chemometrics InterNet Conference, 1994 (http://www.emsl.pnl.gov/docs/incinc/ homepage.html).
30. Doymaz, F.; Romagnoli, J. A.; Palazoglu, A. A Strategy for Detection and Isolation of Sensor Failures and Process Upsets. Chemom. Intell. Lab. Syst. 2001, 55, 109.
31. Kourti, T.; McGregor, J. F. Multivariate SPC Methods for Process and Product Monitoring. J. Qual. Technol. 1996, 28, 409.
32. Garson, D. G. A Comparison of Neural Networks and Expert Systems Algorithms with Common Multivariate Procedures for Analysis of Social Science Data. Social Sci. Comput. Rev. 1991, 9, 400.
33. Mak, B.; Blanning, R. W. An Empirical Measure of Element Contribution in Neural Networks. IEEE Trans. Syst., Man, and Cybernetics C: Appl. Rev. 1998, 28, 561.
34. Chessari, C. J. Studies in Modeling and Advanced Control. Ph.D. Dissertation, University of Sydney, Sydney, Australia, 1995.
35. Doymaz, F.; Chen, J.; Romagnoli, J. A.; Palazoglu, A. A Robust Strategy for Real-Time Process Monitoring. J. Process Control 2001, 11, 343.
36. Holcomb, T.; Morari, M. Local Training of Radial Basis Function Networks: Towards Solving the Hidden Unit Problem. In Proceedings of the ACC; IEEE Press: Piscataway, NJ, 1991; p 2331.
37. Chen, S.; Cowan, C. F. N.; Grant, P. M. Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Trans. Neural Networks 1991, 2, 302.
38. Zhang, J.; Morris, J. A. A Sequential Learning Approach for Single Hidden Layer Neural Networks. Neural Networks 1998, 11, 65.
39. Freund, Y. Boosting a Weak Learning Algorithm by Majority. Inf. Comput. 1995, 121, 256.
40. Breaiman, A. Bagging Predictors. Mach. Learn. 1996, 24, 123.
41. Jolliffe, I. T. Principal Components Analysis; Springer-Verlag: New York, 1986.
42. MATLAB Scientific Computing Language, version 5.3; Mathworks Inc.: Natick, MA, 1999.
43. PLS Toolbox for Use with MATLAB, version 1.5.2; Eigen-vector Technologies: Manson, WA, 1996.
44. Riedmiller, M.; Braun, H. A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In Proceedings of the IEEE International Conference on Neural Networks; IEEE Press: Piscataway, NJ, 1993; Vol. 1, p 586.
45. Linthrust, R. A. Aeration, nitrogen, pH, and salinity as factors affecting Spartina alterniflora growth and dieback. Ph.D. Dissertation, North Carolina State University, Raleigh, NC, 1979.
46. Kramer, M. A. Autoassociative Neural Networks. Comput. Chem. Eng. 1992, 16, 313.
47. Qin, S. J.; Li, W. Detection, Identification, Reconstruction of Faulty Sensors with Maximized Sensitivity, AIChE J. 1999, 45, 1963.
48. Duda, R. O.; Hart, P. E.; Stork, D. G. Pattern Classification, 2nd ed.; John Wiley and Sons: New York, 2001.
49. Raich, A.; Çinar, A. Statistical Process Monitoring and Disturbance Diagnosis in Multivariate Continuous Processes. AIChE J. 1996, 42, 995.
50. Österberg, T.; Fatouros, A.; Mikaelsson, M. Development of a freeze-dried albumin-free formulation of recombinant factor VIII SQ. Pharm. Res. 1997, 14, 892.
51. Boedeker, B. G. D. The Manufacturing of the Recombinant Factor VIII - Kogenate. Transfus. Med. Rev. 1992, 6, 256.