Identification on demand using a blockwise recursive partial least-squares technique

Industrial and Engineering Chemistry Research

Volumn 42, Issue 3, 2003, Pages 540-554

  Vijaysai, P ^a   Gudi, R D ^a   Lakshminarayanan, S ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; ESTIMATION; IDENTIFICATION (CONTROL SYSTEMS); LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MATRIX ALGEBRA; RECURSIVE FUNCTIONS; REGRESSION ANALYSIS;

ONLINE ESTIMATION;

CHEMICAL ENGINEERING;

MODEL;

ADAPTATION; ARTICLE; COVARIANCE; DATA ANALYSIS; LINEAR SYSTEM; MOLECULAR DYNAMICS; PROCESS DESIGN; PROTEIN ANALYSIS; TECHNIQUE;

EID: 0037419837     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie020042r     Document Type: Article

References (23)

1. Ljung, L. System Identification: Theory for the User; Prentice Hall: New York, 1987.
2. Fortescue, T. R.; Kershenbaum, L. S.; Ydstie, B. E. Implementation of Self-Tuning Regulators with Variable Forgetting Factors. Automatica 1981, 17, 831-835.
3. Sripada, N. R.; Fisher, D. G. Improved Least Squares Identification. Int. J. Control 1987, 46 (6), 1889-1913.
4. Niu, S.; Fisher, D. G.; Xiao, D. An Augmented UD Identification Algorithm. Int. J. Control 1992, 56 (1), 193-211.
5. Geladi, P.; Kowalski, B. R. Partial Least-Squares Regression: A Tutorial. Anal. Chim. Acta 1986, 185, 1-17.
6. Ricker, N. L. The Use of Biased Least-Squares Estimators for Parameters in Discrete-Time Pulse-Response Models. Ind. Eng. Chem. Res. 1988, 27, 343-350.
7. Lakshminarayanan, S.; Sirish, S. L.; Nandakumar, K. Modeling and Control of Multivariable Processes: The Dynamic Projections to Latent Structures Approach. AIChE J. 1997, 43, 2307-2322.
8. Dayal, B. S.; MacGregor, J. F. Recursive Exponentially Weighted PLS and its Applications to Adaptive Control and Prediction. J. Process Control 1997, 7, 169-179.
9. Qin, S. J. A Recursive PLS algorithm for Adaptive Data Modeling. Comput. Chem. Eng. 1998, 48, 503-514.
10. Helland, K.; Berntsen, H. E.; Borgen, O. S.; Martens, H. Recursive Algorithm for Partial Least Squares Regression. Chemom. Intell. Lab. Syst. 1992, 14, 129-137.
11. Huang, B. On-line Closed-loop Model Validation and Detection of Abrupt Parameter Changes. J. Process Control 2001, 11, 699-715.
12. Basseville, M.; Nikiforov, I. V. Detection of Abrupt Changes; Prentice Hall: Englewood Cliffs, NJ, 1993.
13. Cybenko, G. Just-in-Time Learning and Estimation. In Identification, Adaptation, and Learning; Bittani, S., Picci, G., Eds.; NATO ASI Series; Springer: New York, 1996; pp 423-434.
14. Hoskuldsson, A. PLS Regression Methods. J. Chemom. 1988, 2, 211-228.
15. Wilkinson, J. H. The Algebraic Eigenvalue Problem; Oxford University Press: New York, 1992.
16. Li, W.; Yue, H. H.; Valle-Cervantes, S.; Qin, S. J. Recursive PCA for Adaptive Process Monitoring. J. Process Control 2000, 10, 471-486.
17. Stenman, A.; Gustafsson, F.; Ljung, L. Just in Time Models for Dynamical Systems. Proceedings of the 35th IEEE Conference on Decision and Control; IEEE Press: Piscataway, NJ, 1996; pp 1115-1120.
18. Dayal, B. S.; MacGregor, J. F. Identification of Finite Impulse Response Models: Methods and Robustness Issues. Ind. Eng. Chem. Res. 1996, 35, 4078-4090.
19. Box, G. E. P.; Jenkins, G. W. Time Series Analysis: Forecasting and Control; Holden Day: San Francisco, 1976,
20. Doyle, F. J.; Ogunnaike, B. A.; Pearson, R. K. Nonlinear Model-based Control Using Second-order Volterra Models. Automatica 1995, 31 (5), 697-714.
21. Ranganathan, S.; Raghunathan, R. Use of Inverse Repeat Sequence (IRS) for Identification in Chemical Process Systems. Ind. Eng. Chem. Res. 1999, 38, 3420-3432.
22. Henson, M. A.; Seborg, D. E. Nonlinear Control Strategies for Continuous Fermenters. Chem. Eng. Sci. 1992, 47, 821-835.
23. Huang, B. Multivariable Model Validation in the Presence of Time Varying Disturbance Dynamics. Chem. Eng. Sci. 2000, 55, 4583-4595.