-
1
-
-
78649468188
-
Review of adaptation mechanisms for data-driven soft sensors
-
[1] Kadlec, P., Grbić, R., Gabrys, B., Review of adaptation mechanisms for data-driven soft sensors. Comput. Chem. Eng. 35 (2011), 1–24.
-
(2011)
Comput. Chem. Eng.
, vol.35
, pp. 1-24
-
-
Kadlec, P.1
Grbić, R.2
Gabrys, B.3
-
2
-
-
71349085411
-
Model optimization of SVM for a fermentation soft sensor
-
[2] Liu, G., Zhou, D., Xu, H., Mei, C., Model optimization of SVM for a fermentation soft sensor. Expert Syst. Appl. 37 (2010), 2708–2713.
-
(2010)
Expert Syst. Appl.
, vol.37
, pp. 2708-2713
-
-
Liu, G.1
Zhou, D.2
Xu, H.3
Mei, C.4
-
3
-
-
84924229688
-
Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process
-
[3] Jin, H., Chen, X., Yang, J., Wang, L., Wu, L., Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process. Chemom. Intell. Lab. Syst. 143 (2015), 58–78.
-
(2015)
Chemom. Intell. Lab. Syst.
, vol.143
, pp. 58-78
-
-
Jin, H.1
Chen, X.2
Yang, J.3
Wang, L.4
Wu, L.5
-
4
-
-
84864805053
-
Soft sensors in bioprocessing: A status report and recommendations
-
[4] Luttmann, R., Bracewell, D.G., Cornelissen, G., Gernaey, K.V., Glassey, J., Hass, V.C., et al. Soft sensors in bioprocessing: A status report and recommendations. Biotechnol. J. 7 (2012), 1040–1048.
-
(2012)
Biotechnol. J.
, vol.7
, pp. 1040-1048
-
-
Luttmann, R.1
Bracewell, D.G.2
Cornelissen, G.3
Gernaey, K.V.4
Glassey, J.5
Hass, V.C.6
-
5
-
-
33746991704
-
Inferential sensors for estimation of polymer quality parameters: Industrial application of a PLS-based soft sensor for a LDPE plant
-
[5] Sharmin, R., Sundararaj, U., Shah, S., Griend, L.V., Sun, Y.-J., Inferential sensors for estimation of polymer quality parameters: Industrial application of a PLS-based soft sensor for a LDPE plant. Chem. Eng. Sci. 61 (2006), 6372–6384.
-
(2006)
Chem. Eng. Sci.
, vol.61
, pp. 6372-6384
-
-
Sharmin, R.1
Sundararaj, U.2
Shah, S.3
Griend, L.V.4
Sun, Y.-J.5
-
6
-
-
84921963027
-
Comparison of variable selection methods for PLS-based soft sensor modeling
-
[6] Wang, Z.X., He, Q.P., Wang, J., Comparison of variable selection methods for PLS-based soft sensor modeling. J. Process Control 26 (2015), 56–72.
-
(2015)
J. Process Control
, vol.26
, pp. 56-72
-
-
Wang, Z.X.1
He, Q.P.2
Wang, J.3
-
7
-
-
84855946000
-
Data-driven prediction of the product formation in industrial 2-keto-L-gulonic acid fermentation
-
[7] Cui, L., Xie, P., Sun, J., Yu, T., Yuan, J., Data-driven prediction of the product formation in industrial 2-keto-L-gulonic acid fermentation. Comput. Chem. Eng. 36 (2012), 386–391.
-
(2012)
Comput. Chem. Eng.
, vol.36
, pp. 386-391
-
-
Cui, L.1
Xie, P.2
Sun, J.3
Yu, T.4
Yuan, J.5
-
8
-
-
84902689450
-
Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote
-
[8] Sun, K., Liu, J., Kang, J.-L., Jang, S.-S., Wong, D.S.-H., Chen, D.-S., Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote. J. Process Control 24 (2014), 1068–1075.
-
(2014)
J. Process Control
, vol.24
, pp. 1068-1075
-
-
Sun, K.1
Liu, J.2
Kang, J.-L.3
Jang, S.-S.4
Wong, D.S.-H.5
Chen, D.-S.6
-
9
-
-
84903588321
-
Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants
-
[9] Kaneko, H., Funatsu, K., Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants. Chemom. Intell. Lab. Syst. 137 (2014), 57–66.
-
(2014)
Chemom. Intell. Lab. Syst.
, vol.137
, pp. 57-66
-
-
Kaneko, H.1
Funatsu, K.2
-
10
-
-
67349089877
-
Data-driven Soft Sensors in the process industry
-
[10] Kadlec, P., Gabrys, B., Strandt, S., Data-driven Soft Sensors in the process industry. Comput. Chem. Eng. 33 (2009), 795–814.
-
(2009)
Comput. Chem. Eng.
, vol.33
, pp. 795-814
-
-
Kadlec, P.1
Gabrys, B.2
Strandt, S.3
-
11
-
-
84905715987
-
Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression
-
[11] Yuan, X., Ge, Z., Song, Z., Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression. Chemom. Intell. Lab. Syst. 138 (2014), 97–109.
-
(2014)
Chemom. Intell. Lab. Syst.
, vol.138
, pp. 97-109
-
-
Yuan, X.1
Ge, Z.2
Song, Z.3
-
12
-
-
36348994942
-
Gaussian process and its application to soft-sensor modeling
-
[12] Wang, H., Gaussian process and its application to soft-sensor modeling. J. Chem. Ind. Eng. 58 (2007), 2840–2845.
-
(2007)
J. Chem. Ind. Eng.
, vol.58
, pp. 2840-2845
-
-
Wang, H.1
-
13
-
-
52949129443
-
Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression
-
[13] Sciascio, F.D., Amicarelli, A.N., Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression. Comput. Chem. Eng. 32 (2008), 3264–3273.
-
(2008)
Comput. Chem. Eng.
, vol.32
, pp. 3264-3273
-
-
Sciascio, F.D.1
Amicarelli, A.N.2
-
14
-
-
84973099950
-
Finite Mixture Models
-
John Wiley & Sons
-
[14] McLachlan, G., Peel, D., Finite Mixture Models. 2004, John Wiley & Sons.
-
(2004)
-
-
McLachlan, G.1
Peel, D.2
-
15
-
-
84905283780
-
Dynamic soft sensor modeling based on state detection and impulses response template
-
[15] Fan, Z., Cao, J., Wei, Y., Dynamic soft sensor modeling based on state detection and impulses response template. Chinese Control and Decision Conference, 2014, 4031–4037.
-
(2014)
Chinese Control and Decision Conference
, pp. 4031-4037
-
-
Fan, Z.1
Cao, J.2
Wei, Y.3
-
16
-
-
84924813762
-
Enhancing dynamic soft sensors based on DPLS: a temporal smoothness regularization approach
-
[16] Bernstein, A., Stein, C., Enhancing dynamic soft sensors based on DPLS: a temporal smoothness regularization approach. J. Process Control 28 (2015), 17–26.
-
(2015)
J. Process Control
, vol.28
, pp. 17-26
-
-
Bernstein, A.1
Stein, C.2
-
17
-
-
33845650294
-
Gaussian Mixture Regression and Classification
-
Rice University
-
[17] Sung, H.G., Gaussian Mixture Regression and Classification. 2004, Rice University.
-
(2004)
-
-
Sung, H.G.1
-
18
-
-
84960497832
-
Software quality prediction using mixture model with EM algorithm
-
[18] Guo, P., Lyu, M.R., Software quality prediction using mixture model with EM algorithm. Proceedings of the First Asia-Pacific Conference on Quality Software, (APAQS 2000), Hong Kong, 2000, 69–78.
-
(2000)
Proceedings of the First Asia-Pacific Conference on Quality Software, (APAQS 2000), Hong Kong
, pp. 69-78
-
-
Guo, P.1
Lyu, M.R.2
-
19
-
-
47549099484
-
Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models
-
[19] Yu, J., Qin, S.J., Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AICHE J. 54 (2008), 1811–1829.
-
(2008)
AICHE J.
, vol.54
, pp. 1811-1829
-
-
Yu, J.1
Qin, S.J.2
-
20
-
-
33646775297
-
“Assumed inherent sensor” inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process
-
[20] Dai, X., Wang, W., Ding, Y., Sun, Z., “Assumed inherent sensor” inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process. Comput. Chem. Eng. 30 (2006), 1203–1225.
-
(2006)
Comput. Chem. Eng.
, vol.30
, pp. 1203-1225
-
-
Dai, X.1
Wang, W.2
Ding, Y.3
Sun, Z.4
-
21
-
-
84988669605
-
Some Aspects on Nonlinear System Identification
-
[21] Ljung, L., Some Aspects on Nonlinear System Identification. 2007.
-
(2007)
-
-
Ljung, L.1
-
22
-
-
4444289955
-
Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis
-
[22] Zamprogna, E., Barolo, M., Seborg, D.E., Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis. J. Process Control 15 (2005), 39–52.
-
(2005)
J. Process Control
, vol.15
, pp. 39-52
-
-
Zamprogna, E.1
Barolo, M.2
Seborg, D.E.3
-
23
-
-
0033036852
-
Variable selection in large environmental data sets using principal components analysis
-
[23] King, J.R., Jackson, D.A., Variable selection in large environmental data sets using principal components analysis. Environmetrics 10 (1999), 67–77.
-
(1999)
Environmetrics
, vol.10
, pp. 67-77
-
-
King, J.R.1
Jackson, D.A.2
-
24
-
-
8744307994
-
Multimodel inference: understanding AIC and BIC in model selection
-
[24] Burnham, K.P., Anderson, D.R., Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33 (2004), 261–304.
-
(2004)
Sociol. Methods Res.
, vol.33
, pp. 261-304
-
-
Burnham, K.P.1
Anderson, D.R.2
-
25
-
-
84938697051
-
Quality prediction in complex batch processes with just-in-time learning model based on non-Gaussian dissimilarity measure
-
[25] Zhang, X., Li, Y., Kano, M., Quality prediction in complex batch processes with just-in-time learning model based on non-Gaussian dissimilarity measure. Ind. Eng. Chem. Res. 54:31 (2015), 7694–7705.
-
(2015)
Ind. Eng. Chem. Res.
, vol.54
, Issue.31
, pp. 7694-7705
-
-
Zhang, X.1
Li, Y.2
Kano, M.3
-
26
-
-
0037110983
-
A modular simulation package for fed-batch fermentation: penicillin production
-
[26] Birol, G., Ündey, C., Cinar, A., A modular simulation package for fed-batch fermentation: penicillin production. Comput. Chem. Eng. 26 (2002), 1553–1565.
-
(2002)
Comput. Chem. Eng.
, vol.26
, pp. 1553-1565
-
-
Birol, G.1
Ündey, C.2
Cinar, A.3
-
27
-
-
77953328215
-
Learning and reproduction of gestures by imitation
-
[27] Calinon, S., D'Halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G., Learning and reproduction of gestures by imitation. IEEE Rob. Autom. Mag. 17 (2010), 44–54.
-
(2010)
IEEE Rob. Autom. Mag.
, vol.17
, pp. 44-54
-
-
Calinon, S.1
D'Halluin, F.2
Sauser, E.L.3
Caldwell, D.G.4
Billard, A.G.5
|