Erratum to "A new process variable and dynamics selection method based on a genetic algorithm-based wavelength selection method" [58, 6, (2012) 1829-1840], DOI: 10.1002/aic.13814;A new process variable and dynamics selection method based on a genetic algorithm-based wavelength selection method

AIChE Journal

Volumn 58, Issue 6, 2012, Pages 1829-1840

  Kaneko, Hiromasa ^a   Funatsu, Kimito ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Maintenance; Process control; Process dynamics; Soft sensor; Variable selection

Indexed keywords

GENETIC ALGORITHMS; MAINTENANCE; PROCESS CONTROL; SPECTRUM ANALYSIS; TIME DELAY;

APPROPRIATE MODELS; EXPLANATORY VARIABLES; INTERPRETATION OF MODELS; PREDICTIVE ABILITIES; PROCESS DYNAMICS; SOFT SENSORS; VARIABLE SELECTION; WAVELENGTH SELECTION;

INDUSTRIAL PLANTS;

EID: 84860639429     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.14266     Document Type: Erratum

References (25)

1. Kano M, Nakagawa Y. Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry. Comput Chem Eng. 2008; 32: 12-24.
2. Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Comput Chem Eng. 2009; 33: 795-814.
3. Kaneko H, Arakawa M, Funatsu K. Development of a new soft sensor method using independent component analysis and partial least squares. AIChE J. 2009; 55: 87-98.
4. Kaneko H, Arakawa M, Funatsu K. Applicability domains and accuracy of prediction of soft sensor models. AIChE J. 2011; 57: 1506-1513.
5. Wold S. Principal component analysis. Chemom Intell Lab Syst. 1987; 2: 37-52.
6. Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst. 2001; 58: 109-130.
7. Chong IG, Jun CH. Performance of some variable selection methods when multicollinearity is present. Chemom Intell Lab Syst. 2005; 78: 103-112.
8. 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.
9. Andersen CM, Bro R. Variable selection in regression-a tutorial. J Chemom. 2010; 24: 728-737.
10. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc. 1996; 58: 267-288.
11. Hocking RR. The analysis and selection of variables in linear regression. Biometrics. 1976; 32: 1-49.
12. Hasegawa K, Miyashita Y, Funatsu K. GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists. J Chem Inf Comput Sci. 1997; 37: 306-310.
13. Kano M, Miyazaki K, Hasebe S, Hashimoto I. Inferential control system of distillation compositions using dynamic partial least squares regression. J Process Control. 2000; 10: 157-166.
14. Rallo R, Ferre GJ, Arenas A, Giralt F. Neural virtual sensor for the inferential prediction of product quality from process variables. Comput Chem Eng. 2002; 26: 1735-1754.
15. Zamprogna E, Barolo M, Seborg DE. Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis. J Process Control. 2005; 15: 39-52.
16. Ni W, Brown SD, Man R. Stacked partial least squares regression analysis for spectral calibration and prediction. J Chemom. 2009; 23: 505-517.
17. Arakawa M, Yamashita Y, Funatsu K, Genetic algorithm-based wavelength selection method for spectral calibration. J Chemom. 2011; 25: 10-19.
18. Faber K, Kowalski BR. Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares. J Chemom. 1997; 11: 181-238.
19. Mallows CL. Some comments on Cp. Technometrics. 1973; 15: 661-675.
20. Akaike H. Factor analysis and AIC. Psychometrika. 1987; 52: 317-332.
21. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978; 6: 461-464.
22. Whitley D. A genetic algorithm tutorial. Stat Comput. 1994; 4: 65-85.
23. Kaneko H, Funatsu K. Maintenance-free soft sensor models with time difference of process variables. Chemom Intell Lab Syst. 2011; 107: 312-317.
24. Houck CR, Joines JA, Kay MG. A Genetic Algorithm for Function Optimization: A Matlab Implementaion. NCSU-IE TR 95-09. Meta-heuristic Research and Applications Group: North Carolina State University, Raleigh, NC, 1995.
25. Vapnik VN. The Nature of Statistical Learning Theory. New York: Springer, 1999.