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Volumn 135, Issue , 2014, Pages 133-140

A unified recursive just-in-time approach with industrial near infrared spectroscopy application

Author keywords

Just in time modeling; Locally weighted PLS; Near infrared spectroscopy; Recursive PLS

Indexed keywords

ALGORITHM; ARTICLE; CONTROLLED STUDY; JUST IN TIME METHOD; NEAR INFRARED SPECTROSCOPY; NONLINEAR SYSTEM; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; SPACE; STATISTICAL MODEL; TIME;

EID: 84899843065     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2014.04.007     Document Type: Article
Times cited : (28)

References (30)
  • 1
    • 78649468188 scopus 로고    scopus 로고
    • Review of adaptation mechanisms for data-driven soft sensors
    • Kadlec P., Grbić R., Gabrys B. Review of adaptation mechanisms for data-driven soft sensors. Comput. Chem. Eng. 2011, 35:1-24.
    • (2011) Comput. Chem. Eng. , vol.35 , pp. 1-24
    • Kadlec, P.1    Grbić, R.2    Gabrys, B.3
  • 2
    • 84879022970 scopus 로고    scopus 로고
    • Recursive wavelength-selection strategy to update near-infrared spectroscopy model with an industrial application
    • Chen M., Khare S., Huang B., Zhang H., Lau E., Feng E. Recursive wavelength-selection strategy to update near-infrared spectroscopy model with an industrial application. Ind. Eng. Chem. Res. 2013, 52(23):7886-7895.
    • (2013) Ind. Eng. Chem. Res. , vol.52 , Issue.23 , pp. 7886-7895
    • Chen, M.1    Khare, S.2    Huang, B.3    Zhang, H.4    Lau, E.5    Feng, E.6
  • 3
    • 67349089877 scopus 로고    scopus 로고
    • Data-driven soft sensors in the process industry
    • Kadlec P., Gabrys B., Strandt S. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 2009, 33:795-814.
    • (2009) Comput. Chem. Eng. , vol.33 , pp. 795-814
    • Kadlec, P.1    Gabrys, B.2    Strandt, S.3
  • 6
    • 0034299906 scopus 로고    scopus 로고
    • Selecting examples for partial memory learning
    • Maloof M.A., Michalski R.S. Selecting examples for partial memory learning. Mach. Learn. 2000, 41:27-52.
    • (2000) Mach. Learn. , vol.41 , pp. 27-52
    • Maloof, M.A.1    Michalski, R.S.2
  • 7
    • 84883713774 scopus 로고    scopus 로고
    • Learning drifting concepts: example selection vs. example weighting
    • Klinkenberg R. Learning drifting concepts: example selection vs. example weighting. Intell. Data Anal. 2004, 8:281-300.
    • (2004) Intell. Data Anal. , vol.8 , pp. 281-300
    • Klinkenberg, R.1
  • 8
    • 41849096368 scopus 로고    scopus 로고
    • Boosting classifiers for drifting concepts
    • Scholz M., Klinkenberg R. Boosting classifiers for drifting concepts. Intell. Data Anal. 2007, 11:3-28.
    • (2007) Intell. Data Anal. , vol.11 , pp. 3-28
    • Scholz, M.1    Klinkenberg, R.2
  • 9
    • 20344389745 scopus 로고    scopus 로고
    • Application of a moving-window-adaptive neural network to the modeling of a full-scale anaerobic filter process
    • Lee M.W., Joung J.Y., Lee D.S., Park J.M., Woo S.H. Application of a moving-window-adaptive neural network to the modeling of a full-scale anaerobic filter process. Ind. Eng. Chem. Res. 2005, 44:3973-3982.
    • (2005) Ind. Eng. Chem. Res. , vol.44 , pp. 3973-3982
    • Lee, M.W.1    Joung, J.Y.2    Lee, D.S.3    Park, J.M.4    Woo, S.H.5
  • 10
    • 22944436794 scopus 로고    scopus 로고
    • Process monitoring approach using fast moving window PCA
    • Wang X., Kruger U., Irwin G.W. Process monitoring approach using fast moving window PCA. Ind. Eng. Chem. Res. 2005, 44:5691-5702.
    • (2005) Ind. Eng. Chem. Res. , vol.44 , pp. 5691-5702
    • Wang, X.1    Kruger, U.2    Irwin, G.W.3
  • 11
    • 64249101035 scopus 로고    scopus 로고
    • Moving window kernel PCA for adaptive monitoring of nonlinear processes
    • Liu X., Kruger U., Littler T., Xie L., Wang S. Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemom. Intell. Lab. Syst. 2009, 96:132-143.
    • (2009) Chemom. Intell. Lab. Syst. , vol.96 , pp. 132-143
    • Liu, X.1    Kruger, U.2    Littler, T.3    Xie, L.4    Wang, S.5
  • 12
    • 14944347949 scopus 로고    scopus 로고
    • A recursive nonlinear PLS algorithm for adaptive nonlinear process modeling
    • Li C., Ye H., Wang G., Zhang J. A recursive nonlinear PLS algorithm for adaptive nonlinear process modeling. Chem. Eng. Technol. 2005, 28:141-152.
    • (2005) Chem. Eng. Technol. , vol.28 , pp. 141-152
    • Li, C.1    Ye, H.2    Wang, G.3    Zhang, J.4
  • 13
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data modeling
    • Joe Qin S. Recursive PLS algorithms for adaptive data modeling. Comput. Chem. Eng. 1998, 22:503-514.
    • (1998) Comput. Chem. Eng. , vol.22 , pp. 503-514
    • Joe Qin, S.1
  • 14
    • 79961019569 scopus 로고    scopus 로고
    • Modeling of fermentation processes using online kernel learning algorithm
    • Proceedings of IFAC World Congress
    • Y. Liu, H. Wang, P. Li, Modeling of fermentation processes using online kernel learning algorithm, in: Proceedings of IFAC World Congress, 2008, pp. 9679-9684.
    • (2008) , pp. 9679-9684
    • Liu, Y.1    Wang, H.2    Li, P.3
  • 15
    • 2342655711 scopus 로고    scopus 로고
    • Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes
    • Chauchard F., Cogdill R., Roussel S., Roger J., Bellon-Maurel V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemom. Intell. Lab. Syst. 2004, 71:141-150.
    • (2004) Chemom. Intell. Lab. Syst. , vol.71 , pp. 141-150
    • Chauchard, F.1    Cogdill, R.2    Roussel, S.3    Roger, J.4    Bellon-Maurel, V.5
  • 16
    • 33749472827 scopus 로고    scopus 로고
    • Kernel classifier with adaptive structure and fixed memory for process diagnosis
    • Wang H., Li P., Gao F., Song Z., Ding S.X. Kernel classifier with adaptive structure and fixed memory for process diagnosis. AIChE J. 2006, 52:3515-3531.
    • (2006) AIChE J. , vol.52 , pp. 3515-3531
    • Wang, H.1    Li, P.2    Gao, F.3    Song, Z.4    Ding, S.X.5
  • 17
    • 14844303316 scopus 로고    scopus 로고
    • Nonlinear process monitoring using JITL-PCA
    • Cheng C., Chiu M.-S. Nonlinear process monitoring using JITL-PCA. Chemom. Intell. Lab. Syst. 2005, 76:1-13.
    • (2005) Chemom. Intell. Lab. Syst. , vol.76 , pp. 1-13
    • Cheng, C.1    Chiu, M.-S.2
  • 18
    • 80052809967 scopus 로고    scopus 로고
    • Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes
    • Chen K., Ji J., Wang H., Liu Y., Song Z. Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes. Chem. Eng. Res. Des. 2011, 89:2117-2124.
    • (2011) Chem. Eng. Res. Des. , vol.89 , pp. 2117-2124
    • Chen, K.1    Ji, J.2    Wang, H.3    Liu, Y.4    Song, Z.5
  • 19
    • 8444241860 scopus 로고    scopus 로고
    • Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
    • Cawley G.C., Talbot N.L. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw. 2004, 17:1467-1475.
    • (2004) Neural Netw. , vol.17 , pp. 1467-1475
    • Cawley, G.C.1    Talbot, N.L.2
  • 20
    • 84863357539 scopus 로고    scopus 로고
    • Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes
    • Liu Y., Gao Z., Li P., Wang H. Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes. Ind. Eng. Chem. Res. 2012, 51:4313-4327.
    • (2012) Ind. Eng. Chem. Res. , vol.51 , pp. 4313-4327
    • Liu, Y.1    Gao, Z.2    Li, P.3    Wang, H.4
  • 21
    • 78650524009 scopus 로고    scopus 로고
    • A comparative study of just-in-time-learning based methods for online soft sensor modeling
    • Ge Z., Song Z. A comparative study of just-in-time-learning based methods for online soft sensor modeling. Chemom. Intell. Lab. Syst. 2010, 104:306-317.
    • (2010) Chemom. Intell. Lab. Syst. , vol.104 , pp. 306-317
    • Ge, Z.1    Song, Z.2
  • 22
    • 81755166220 scopus 로고    scopus 로고
    • Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection
    • Kim S., Kano M., Nakagawa H., Hasebe S. Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection. Int. J. Pharm. 2011, 421:269-274.
    • (2011) Int. J. Pharm. , vol.421 , pp. 269-274
    • Kim, S.1    Kano, M.2    Nakagawa, H.3    Hasebe, S.4
  • 23
    • 0036639869 scopus 로고    scopus 로고
    • Scalable techniques from nonparametric statistics for real time robot learning
    • Schaal S., Atkeson C.G., Vijayakumar S. Scalable techniques from nonparametric statistics for real time robot learning. Appl. Intell. 2002, 17:49-60.
    • (2002) Appl. Intell. , vol.17 , pp. 49-60
    • Schaal, S.1    Atkeson, C.G.2    Vijayakumar, S.3
  • 24
    • 0033970548 scopus 로고    scopus 로고
    • NIR calibration in non-linear systems: different PLS approaches and artificial neural networks
    • Blanco M., Coello J., Iturriaga H., Maspoch S., Pages J. NIR calibration in non-linear systems: different PLS approaches and artificial neural networks. Chemom. Intell. Lab. Syst. 2000, 50:75-82.
    • (2000) Chemom. Intell. Lab. Syst. , vol.50 , pp. 75-82
    • Blanco, M.1    Coello, J.2    Iturriaga, H.3    Maspoch, S.4    Pages, J.5
  • 25
    • 0028299952 scopus 로고
    • New approach for distance measurement in locally weighted regression
    • Wang Z., Isaksson T., Kowalski B.R. New approach for distance measurement in locally weighted regression. Anal. Chem. 1994, 66:249-260.
    • (1994) Anal. Chem. , vol.66 , pp. 249-260
    • Wang, Z.1    Isaksson, T.2    Kowalski, B.R.3
  • 27
    • 84876727380 scopus 로고    scopus 로고
    • Development of soft-sensor using locally weighted PLS with adaptive similarity measure
    • Kim S., Okajima R., Kano M., Hasebe S. Development of soft-sensor using locally weighted PLS with adaptive similarity measure. Chemom. Intell. Lab. Syst. 2013, 124:43-49.
    • (2013) Chemom. Intell. Lab. Syst. , vol.124 , pp. 43-49
    • Kim, S.1    Okajima, R.2    Kano, M.3    Hasebe, S.4
  • 28
    • 0031168001 scopus 로고    scopus 로고
    • Recursive exponentially weighted PLS and its applications to adaptive control and prediction
    • Dayal B., MacGregor J. Recursive exponentially weighted PLS and its applications to adaptive control and prediction. J. Process Control 1997, 7:169-179.
    • (1997) J. Process Control , vol.7 , pp. 169-179
    • Dayal, B.1    MacGregor, J.2
  • 29
    • 0002815256 scopus 로고    scopus 로고
    • Improved PLS algorithms
    • Dayal B., MacGregor J. Improved PLS algorithms. J. Chemom. 1998, 11:73-85.
    • (1998) J. Chemom. , vol.11 , pp. 73-85
    • Dayal, B.1    MacGregor, J.2
  • 30
    • 0042553279 scopus 로고
    • Smoothing and differentiation of data by simplified least squares procedures
    • Savitzky A., Golay M. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36(8):1627-1639.
    • (1964) Anal. Chem. , vol.36 , Issue.8 , pp. 1627-1639
    • Savitzky, A.1    Golay, M.2


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