-
1
-
-
0141427255
-
-
CRC Press: New York.
-
Cinar, A.; Parulekar, S. J.; Undey, C.; Birol, G. Batch Fermentation: Modeling: Monitoring, and Control; CRC Press: New York, 2003.
-
(2003)
Batch Fermentation: Modeling: Monitoring, and Control
-
-
Cinar, A.1
Parulekar, S.J.2
Undey, C.3
Birol, G.4
-
2
-
-
84984100048
-
The use of secondary measurements to improve control
-
Weber, R.; Brosilow, C. The use of secondary measurements to improve control AIChE J. 1972, 18, 614-623 10.1002/aic.690180323
-
(1972)
AIChE J.
, vol.18
, pp. 614-623
-
-
Weber, R.1
Brosilow, C.2
-
3
-
-
0017972806
-
Inferential control of processes: Part I. Steady state analysis and design
-
Joseph, B.; Brosilow, C. B. Inferential control of processes: Part I. Steady state analysis and design AIChE J. 1978, 24, 485-492 10.1002/aic.690240313
-
(1978)
AIChE J.
, vol.24
, pp. 485-492
-
-
Joseph, B.1
Brosilow, C.B.2
-
4
-
-
0017972807
-
Inferential control of processes: Part II. The structure and dynamics of inferential control systems
-
Brosilow, C.; Tong, M. Inferential control of processes: Part II. The structure and dynamics of inferential control systems AIChE J. 1978, 24, 492-500 10.1002/aic.690240314
-
(1978)
AIChE J.
, vol.24
, pp. 492-500
-
-
Brosilow, C.1
Tong, M.2
-
5
-
-
0017971592
-
Inferential control of processes: Part III. Construction of optimal and suboptimal dynamic estimators
-
Joseph, B.; Brosilow, C. Inferential control of processes: Part III. Construction of optimal and suboptimal dynamic estimators AIChE J. 1978, 24, 500-509 10.1002/aic.690240315
-
(1978)
AIChE J.
, vol.24
, pp. 500-509
-
-
Joseph, B.1
Brosilow, C.2
-
6
-
-
0002111519
-
Soft-sensors for process estimation and inferential control
-
Tham, M. T.; Montague, G. A.; Morris, A. J.; Lant, P. A. Soft-sensors for process estimation and inferential control J. Process Control 1991, 1, 3-14 10.1016/0959-1524(91)87002-F
-
(1991)
J. Process Control
, vol.1
, pp. 3-14
-
-
Tham, M.T.1
Montague, G.A.2
Morris, A.J.3
Lant, P.A.4
-
7
-
-
0028460607
-
Development of inferential process models using PLS
-
Kresta, J.; Marlin, T.; MacGregor, J. Development of inferential process models using PLS Comput. Chem. Eng. 1994, 18, 597-611 10.1016/0098-1354(93)E0006-U
-
(1994)
Comput. Chem. Eng.
, vol.18
, pp. 597-611
-
-
Kresta, J.1
Marlin, T.2
MacGregor, J.3
-
8
-
-
0034661135
-
A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns
-
Park, S.; Han, C. A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns Comput. Chem. Eng. 2000, 24, 871-877 10.1016/S0098-1354(00)00343-4
-
(2000)
Comput. Chem. Eng.
, vol.24
, pp. 871-877
-
-
Park, S.1
Han, C.2
-
10
-
-
33847162850
-
A systematic approach for soft sensor development
-
Lin, B.; Recke, B.; Knudsen, J. K.; Jørgensen, S. B. A systematic approach for soft sensor development Comput. Chem. Eng. 2007, 31, 419-425 10.1016/j.compchemeng.2006.05.030
-
(2007)
Comput. Chem. Eng.
, vol.31
, pp. 419-425
-
-
Lin, B.1
Recke, B.2
Knudsen, J.K.3
Jørgensen, S.B.4
-
11
-
-
35548968908
-
Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry
-
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 10.1016/j.compchemeng.2007.07.005
-
(2008)
Comput. Chem. Eng.
, vol.32
, pp. 12-24
-
-
Kano, M.1
Nakagawa, Y.2
-
12
-
-
67349089877
-
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 10.1016/j.compchemeng.2008.12.012
-
(2009)
Comput. Chem. Eng.
, vol.33
, pp. 795-814
-
-
Kadlec, P.1
Gabrys, B.2
Strandt, S.3
-
13
-
-
84872920533
-
Virtual sensing technology in process industries: Trends and challenges revealed by recent industrial applications
-
Kano, M.; Fujiwara, K. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications J. Chem. Eng. Jpn. 2013, 46, 1-17 10.1252/jcej.12we167
-
(2013)
J. Chem. Eng. Jpn.
, vol.46
, pp. 1-17
-
-
Kano, M.1
Fujiwara, K.2
-
15
-
-
68049143320
-
Soft-sensor development using correlation-based just-in-time modeling
-
Fujiwara, K.; Kano, M.; Hasebe, S.; Takinami, A. Soft-sensor development using correlation-based just-in-time modeling AIChE J. 2009, 55, 1754-1765 10.1002/aic.11791
-
(2009)
AIChE J.
, vol.55
, pp. 1754-1765
-
-
Fujiwara, K.1
Kano, M.2
Hasebe, S.3
Takinami, A.4
-
16
-
-
84889685311
-
External analysis-based regression model for robust soft sensing of multimode chemical processes
-
Ge, Z.; Song, Z.; Kano, M. External analysis-based regression model for robust soft sensing of multimode chemical processes AIChE J. 2014, 60, 136-147 10.1002/aic.14253
-
(2014)
AIChE J.
, vol.60
, pp. 136-147
-
-
Ge, Z.1
Song, Z.2
Kano, M.3
-
17
-
-
84892445860
-
Mixture semisupervised principal component regression model and soft sensor application
-
Ge, Z.; Huang, B.; Song, Z. Mixture semisupervised principal component regression model and soft sensor application AIChE J. 2014, 60, 533-545 10.1002/aic.14270
-
(2014)
AIChE J.
, vol.60
, pp. 533-545
-
-
Ge, Z.1
Huang, B.2
Song, Z.3
-
18
-
-
0035965476
-
PLS-regression: A basic tool of chemometrics
-
Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: a basic tool of chemometrics Chemom. Intell. Lab. Syst. 2001, 58, 109-130 10.1016/S0169-7439(01)00155-1
-
(2001)
Chemom. Intell. Lab. Syst.
, vol.58
, pp. 109-130
-
-
Wold, S.1
Sjöström, M.2
Eriksson, L.3
-
19
-
-
79954599740
-
Local learning-based adaptive soft sensor for catalyst activation prediction
-
Kadlec, P.; Gabrys, B. Local learning-based adaptive soft sensor for catalyst activation prediction AIChE J. 2011, 57, 1288-1301 10.1002/aic.12346
-
(2011)
AIChE J.
, vol.57
, pp. 1288-1301
-
-
Kadlec, P.1
Gabrys, B.2
-
20
-
-
84862208873
-
Localized, adaptive recursive partial least squares regression for dynamic system modeling
-
Ni, W.; Tan, S. K.; Ng, W. J.; Brown, S. D. Localized, adaptive recursive partial least squares regression for dynamic system modeling Ind. Eng. Chem. Res. 2012, 51, 8025-8039 10.1021/ie203043q
-
(2012)
Ind. Eng. Chem. Res.
, vol.51
, pp. 8025-8039
-
-
Ni, W.1
Tan, S.K.2
Ng, W.J.3
Brown, S.D.4
-
21
-
-
84896913551
-
A localized adaptive soft sensor for dynamic system modeling
-
Ni, W.; Br own, S. D.; Man, R. A localized adaptive soft sensor for dynamic system modeling Chem. Eng. Sci. 2014, 111, 350-363 10.1016/j.ces.2014.03.002
-
(2014)
Chem. Eng. Sci.
, vol.111
, pp. 350-363
-
-
Ni, W.1
Brown, S.D.2
Man, R.3
-
22
-
-
84883736569
-
Long-Term Industrial Applications of Inferential Control Based on Just-In-Time Soft-Sensors: Economical Impact and Challenges
-
Kim, S.; Kano, M.; Hasebe, S.; Takinami, A.; Seki, T. Long-Term Industrial Applications of Inferential Control Based on Just-In-Time Soft-Sensors: Economical Impact and Challenges Ind. Eng. Chem. Res. 2013, 52, 12346-12356 10.1021/ie303488m
-
(2013)
Ind. Eng. Chem. Res.
, vol.52
, pp. 12346-12356
-
-
Kim, S.1
Kano, M.2
Hasebe, S.3
Takinami, A.4
Seki, T.5
-
23
-
-
84924229688
-
Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process
-
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. 2015, 143, 58-78 10.1016/j.chemolab.2015.02.018
-
(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
-
24
-
-
84906316402
-
Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division
-
Shao, W.; Tian, X.; Wang, P. Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division Chin. J. Chem. Eng. 2014, 22, 828-836 10.1016/j.cjche.2014.05.003
-
(2014)
Chin. J. Chem. Eng.
, vol.22
, pp. 828-836
-
-
Shao, W.1
Tian, X.2
Wang, P.3
-
25
-
-
84961290207
-
Adaptive Soft Sensor for Quality Prediction of Chemical Processes Based on Selective Ensemble of Local Partial Least Squares Models
-
Shao, W.; Tian, X. Adaptive Soft Sensor for Quality Prediction of Chemical Processes Based on Selective Ensemble of Local Partial Least Squares Models Chem. Eng. Res. Des. 2015, 95, 113-132 10.1016/j.cherd.2015.01.006
-
(2015)
Chem. Eng. Res. Des.
, vol.95
, pp. 113-132
-
-
Shao, W.1
Tian, X.2
-
26
-
-
58449118276
-
Development of a new soft sensor method using independent component analysis and partial least squares
-
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 10.1002/aic.11648
-
(2009)
AIChE J.
, vol.55
, pp. 87-98
-
-
Kaneko, H.1
Arakawa, M.2
Funatsu, K.3
-
27
-
-
84887725182
-
Ensemble independent component regression models and soft sensing application
-
Ge, Z.; Song, Z. Ensemble independent component regression models and soft sensing application Chemom. Intell. Lab. Syst. 2014, 130, 115-122 10.1016/j.chemolab.2013.09.009
-
(2014)
Chemom. Intell. Lab. Syst.
, vol.130
, pp. 115-122
-
-
Ge, Z.1
Song, Z.2
-
28
-
-
84894317151
-
Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes
-
Ge, Z.; Song, Z.; Wang, P. Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes Chem. Eng. Res. Des. 2014, 92, 509-521 10.1016/j.cherd.2013.09.010
-
(2014)
Chem. Eng. Res. Des.
, vol.92
, pp. 509-521
-
-
Ge, Z.1
Song, Z.2
Wang, P.3
-
29
-
-
84906872234
-
Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes
-
Yuan, X.; Ge, Z.; Song, Z. Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes Ind. Eng. Chem. Res. 2014, 53, 13736-13749 10.1021/ie4041252
-
(2014)
Ind. Eng. Chem. Res.
, vol.53
, pp. 13736-13749
-
-
Yuan, X.1
Ge, Z.2
Song, Z.3
-
30
-
-
0038259120
-
Kernel partial least squares regression in reproducing kernel hilbert space
-
Rosipal, R.; Trejo, L. J. Kernel partial least squares regression in reproducing kernel hilbert space Journal of Machine Learning Research 2001, 2, 97-123
-
(2001)
Journal of Machine Learning Research
, vol.2
, pp. 97-123
-
-
Rosipal, R.1
Trejo, L.J.2
-
31
-
-
84868224530
-
Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes
-
Yu, J. Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes Ind. Eng. Chem. Res. 2012, 51, 13227-13237 10.1021/ie3020186
-
(2012)
Ind. Eng. Chem. Res.
, vol.51
, pp. 13227-13237
-
-
Yu, J.1
-
32
-
-
84905686213
-
Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes
-
Jin, H.; Chen, X.; Yang, J.; Wu, L. Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes Comput. Chem. Eng. 2014, 71, 77-93 10.1016/j.compchemeng.2014.07.014
-
(2014)
Comput. Chem. Eng.
, vol.71
, pp. 77-93
-
-
Jin, H.1
Chen, X.2
Yang, J.3
Wu, L.4
-
33
-
-
57049112694
-
ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process
-
Gonzaga, J.; Meleiro, L.; Kiang, C.; Maciel Filho, R. ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process Comput. Chem. Eng. 2009, 33, 43-49 10.1016/j.compchemeng.2008.05.019
-
(2009)
Comput. Chem. Eng.
, vol.33
, pp. 43-49
-
-
Gonzaga, J.1
Meleiro, L.2
Kiang, C.3
Maciel Filho, R.4
-
34
-
-
78650945964
-
Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression
-
Niu, D.-p.; Wang, F.-l.; Zhang, L.-l.; He, D.-k.; Jia, M.-x. Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression Chemom. Intell. Lab. Syst. 2011, 105, 125-130 10.1016/j.chemolab.2010.11.007
-
(2011)
Chemom. Intell. Lab. Syst.
, vol.105
, pp. 125-130
-
-
Niu, D.-P.1
Wang, F.-L.2
Zhang, L.-L.3
He D.-k.4
Jia, M.-X.5
-
35
-
-
84855946000
-
Data-driven prediction of the product formation in industrial 2-keto-L-gulonic acid fermentation
-
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. 2012, 36, 386-391 10.1016/j.compchemeng.2011.06.012
-
(2012)
Comput. Chem. Eng.
, vol.36
, pp. 386-391
-
-
Cui, L.1
Xie, P.2
Sun, J.3
Yu, T.4
Yuan, J.5
-
36
-
-
27444433806
-
Soft-sensor development for fed-batch bioreactors using support vector regression
-
Desai, K.; Badhe, Y.; Tambe, S. S.; Kulkarni, B. D. Soft-sensor development for fed-batch bioreactors using support vector regression Biochem. Eng. J. 2006, 27, 225-239 10.1016/j.bej.2005.08.002
-
(2006)
Biochem. Eng. J.
, vol.27
, pp. 225-239
-
-
Desai, K.1
Badhe, Y.2
Tambe, S.S.3
Kulkarni, B.D.4
-
37
-
-
33947266512
-
Development of a soft sensor for a batch distillation column using support vector regression techniques
-
Jain, P.; Rahman, I.; Kulkarni, B. Development of a soft sensor for a batch distillation column using support vector regression techniques Chem. Eng. Res. Des. 2007, 85, 283-287 10.1205/cherd05026
-
(2007)
Chem. Eng. Res. Des.
, vol.85
, pp. 283-287
-
-
Jain, P.1
Rahman, I.2
Kulkarni, B.3
-
38
-
-
84859392648
-
A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses
-
Yu, J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses Comput. Chem. Eng. 2012, 41, 134-144 10.1016/j.compchemeng.2012.03.004
-
(2012)
Comput. Chem. Eng.
, vol.41
, pp. 134-144
-
-
Yu, J.1
-
39
-
-
84892441284
-
Application of online support vector regression for soft sensors
-
Kaneko, H.; Funatsu, K. Application of online support vector regression for soft sensors AIChE J. 2014, 60, 600-612 10.1002/aic.14299
-
(2014)
AIChE J.
, vol.60
, pp. 600-612
-
-
Kaneko, H.1
Funatsu, K.2
-
40
-
-
84903588321
-
Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants
-
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. 2014, 137, 57-66 10.1016/j.chemolab.2014.06.008
-
(2014)
Chemom. Intell. Lab. Syst.
, vol.137
, pp. 57-66
-
-
Kaneko, H.1
Funatsu, K.2
-
41
-
-
84928348714
-
Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes
-
Jin, H.; Chen, X.; Yang, J.; Zhang, H.; Wang, L.; Wu, L. Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes Chem. Eng. Sci. 2015, 131, 282-303 10.1016/j.ces.2015.03.038
-
(2015)
Chem. Eng. Sci.
, vol.131
, pp. 282-303
-
-
Jin, H.1
Chen, X.2
Yang, J.3
Zhang, H.4
Wang, L.5
Wu, L.6
-
42
-
-
84863357539
-
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 10.1021/ie201650u
-
(2012)
Ind. Eng. Chem. Res.
, vol.51
, pp. 4313-4327
-
-
Liu, Y.1
Gao, Z.2
Li, P.3
Wang, H.4
-
43
-
-
84879060636
-
Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes
-
Liu, Y.; Chen, J. Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes J. Process Control 2013, 23, 793-804 10.1016/j.jprocont.2013.03.008
-
(2013)
J. Process Control
, vol.23
, pp. 793-804
-
-
Liu, Y.1
Chen, J.2
-
45
-
-
61349165676
-
Multiple model soft sensor based on affinity propagation, Gaussian process and Bayesian committee machine
-
Li, X.; Su, H.; Chu, J. Multiple model soft sensor based on affinity propagation, gaussian process and bayesian committee machine Chin. J. Chem. Eng. 2009, 17, 95-99 10.1016/S1004-9541(09)60039-2
-
(2009)
Chin. J. Chem. Eng.
, vol.17
, pp. 95-99
-
-
Li, X.1
Su, H.2
Chu, J.3
-
46
-
-
61849183105
-
Bagging for Gaussian process regression
-
Chen, T.; Ren, J. Bagging for Gaussian process regression Neurocomputing 2009, 72, 1605-1610 10.1016/j.neucom.2008.09.002
-
(2009)
Neurocomputing
, vol.72
, pp. 1605-1610
-
-
Chen, T.1
Ren, J.2
-
47
-
-
84864805251
-
Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach
-
Yu, J. Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach Chem. Eng. Sci. 2012, 82, 22-30 10.1016/j.ces.2012.07.018
-
(2012)
Chem. Eng. Sci.
, vol.82
, pp. 22-30
-
-
Yu, J.1
-
48
-
-
84874515333
-
A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty
-
Yu, J.; Chen, K.; Rashid, M. M. A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty Chem. Eng. Sci. 2013, 93, 96-109 10.1016/j.ces.2013.01.058
-
(2013)
Chem. Eng. Sci.
, vol.93
, pp. 96-109
-
-
Yu, J.1
Chen, K.2
Rashid, M.M.3
-
49
-
-
84880339799
-
Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models
-
Grbić, R.; Slišković, D.; Kadlec, P. Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models Comput. Chem. Eng. 2013, 58, 84-97 10.1016/j.compchemeng.2013.06.014
-
(2013)
Comput. Chem. Eng.
, vol.58
, pp. 84-97
-
-
Grbić, R.1
Slišković, D.2
Kadlec, P.3
-
50
-
-
84912562670
-
Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models
-
Liu, Y.; Gao, Z. Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models. J. Appl. Polym. Sci. 2015, 132, http://dx.doi.org/10.1002/app.41432, 10.1002/app.41432.
-
(2015)
J. Appl. Polym. Sci.
, vol.132
-
-
Liu, Y.1
Gao, Z.2
-
51
-
-
84924482066
-
Industrial melt index prediction with the ensemble anti-outlier just-in-time Gaussian process regression modeling method
-
Liu, Y.; Gao, Z. Industrial melt index prediction with the ensemble anti-outlier just-in-time Gaussian process regression modeling method. J. Appl. Polym. Sci. 2015, 132, http://dx.doi.org/10.1002/app.41958, 10.1002/app.41958.
-
(2015)
J. Appl. Polym. Sci.
, vol.132
-
-
Liu, Y.1
Gao, Z.2
-
52
-
-
80655146441
-
Design and application of soft sensor using ensemble methods
-
Toulouse, France, September 5-9
-
Soares, S.; Araújo, R.; Sousa, P.; Souza, F. Design and application of soft sensor using ensemble methods. 2011 IEEE 16th Conference on Emerging Technologies & Factory Automation (ETFA, Toulouse, France, September 5-9, 2011; pp 1-8.
-
(2011)
2011 IEEE 16th Conference on Emerging Technologies & Factory Automation (ETFA
, pp. 1-8
-
-
Soares, S.1
Araújo, R.2
Sousa, P.3
Souza, F.4
-
53
-
-
0031074521
-
1997. Locally weighted learning
-
Atkeson, C. G.; Moore, A. W.; Schaal, S. 1997. Locally weighted learning Artificial Intelligence Review 1997, 11, 11-73 10.1023/A:1006559212014
-
(1997)
Artificial Intelligence Review
, vol.11
, pp. 11-73
-
-
Atkeson, C.G.1
Moore, A.W.2
Schaal, S.3
-
54
-
-
0036639869
-
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 Applied Intelligence 2002, 17, 49-60 10.1023/A:1015727715131
-
(2002)
Applied Intelligence
, vol.17
, pp. 49-60
-
-
Schaal, S.1
Atkeson, C.G.2
Vijayakumar, S.3
-
55
-
-
2942558590
-
A new data-based methodology for nonlinear process modeling
-
Cheng, C.; Chiu, M.-S. A new data-based methodology for nonlinear process modeling Chem. Eng. Sci. 2004, 59, 2801-2810 10.1016/j.ces.2004.04.020
-
(2004)
Chem. Eng. Sci.
, vol.59
, pp. 2801-2810
-
-
Cheng, C.1
Chiu, M.-S.2
-
56
-
-
80052809967
-
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 10.1016/j.cherd.2011.01.032
-
(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
-
57
-
-
77956444702
-
The state of the art in chemical process control in Japan: Good practice and questionnaire survey
-
Kano, M.; Ogawa, M. The state of the art in chemical process control in Japan: Good practice and questionnaire survey J. Process Control 2010, 20, 969-982 10.1016/j.jprocont.2010.06.013
-
(2010)
J. Process Control
, vol.20
, pp. 969-982
-
-
Kano, M.1
Ogawa, M.2
-
58
-
-
26444562687
-
The problem of concept drift: Definitions and related work
-
Department of Computer Science, Trinity College Dublin, The University of Dublin: Ireland.
-
Tsymbal, A. The problem of concept drift: definitions and related work. Technical Report; Department of Computer Science, Trinity College Dublin, The University of Dublin: Ireland, 2004.
-
(2004)
Technical Report
-
-
Tsymbal, A.1
-
59
-
-
78649468188
-
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 10.1016/j.compchemeng.2010.07.034
-
(2011)
Comput. Chem. Eng.
, vol.35
, pp. 1-24
-
-
Kadlec, P.1
Grbić, R.2
Gabrys, B.3
-
60
-
-
84879309312
-
Classification of the degradation of soft sensor models and discussion on adaptive models
-
Kaneko, H.; Funatsu, K. Classification of the degradation of soft sensor models and discussion on adaptive models AIChE J. 2013, 59, 2339-2347 10.1002/aic.14006
-
(2013)
AIChE J.
, vol.59
, pp. 2339-2347
-
-
Kaneko, H.1
Funatsu, K.2
-
61
-
-
22944436794
-
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 10.1021/ie048873f
-
(2005)
Ind. Eng. Chem. Res.
, vol.44
, pp. 5691-5702
-
-
Wang, X.1
Kruger, U.2
Irwin, G.W.3
-
62
-
-
84861071787
-
Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing
-
Ni, W.; Tan, S. K.; Ng, W. J.; Brown, S. D. Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing Ind. Eng. Chem. Res. 2012, 51, 6416-6428 10.1021/ie201898a
-
(2012)
Ind. Eng. Chem. Res.
, vol.51
, pp. 6416-6428
-
-
Ni, W.1
Tan, S.K.2
Ng, W.J.3
Brown, S.D.4
-
63
-
-
84873603969
-
-
2012 11th International Conference on Machine Learning and Applications (ICMLA), Boca Raton, Florida, USA, December 12-15
-
Grbic, R.; Sliskovic, D.; Kadlec, P. Adaptive soft sensor for online prediction based on moving window Gaussian process regression. 2012 11th International Conference on Machine Learning and Applications (ICMLA), Boca Raton, Florida, USA, December 12-15, 2012; pp 428-433.
-
(2012)
Adaptive Soft Sensor for Online Prediction Based on Moving Window Gaussian Process Regression
, pp. 428-433
-
-
Grbic, R.1
Sliskovic, D.2
Kadlec, P.3
-
64
-
-
84921634883
-
Moving window and just-in-time soft sensor model based on time differences considering a small number of measurements
-
Kaneko, H.; Funatsu, K. Moving window and just-in-time soft sensor model based on time differences considering a small number of measurements Ind. Eng. Chem. Res. 2015, 54, 700-704 10.1021/ie503962e
-
(2015)
Ind. Eng. Chem. Res.
, vol.54
, pp. 700-704
-
-
Kaneko, H.1
Funatsu, K.2
-
65
-
-
0032044750
-
Recursive PLS algorithms for adaptive data modeling
-
Qin, S. J. Recursive PLS algorithms for adaptive data modeling Comput. Chem. Eng. 1998, 22, 503-514 10.1016/S0098-1354(97)00262-7
-
(1998)
Comput. Chem. Eng.
, vol.22
, pp. 503-514
-
-
Qin, S.J.1
-
66
-
-
33645417998
-
Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid (PTA) process
-
Mu, S.; Zeng, Y.; Liu, R.; Wu, P.; Su, H.; Chu, J. Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid (PTA) process J. Process Control 2006, 16, 557-566 10.1016/j.jprocont.2005.11.004
-
(2006)
J. Process Control
, vol.16
, pp. 557-566
-
-
Mu, S.1
Zeng, Y.2
Liu, R.3
Wu, P.4
Su, H.5
Chu, J.6
-
67
-
-
59349107270
-
A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant
-
Ahmed, F.; Nazir, S.; Yeo, Y. K. A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant Korean J. Chem. Eng. 2009, 26, 14-20 10.1007/s11814-009-0003-3
-
(2009)
Korean J. Chem. Eng.
, vol.26
, pp. 14-20
-
-
Ahmed, F.1
Nazir, S.2
Yeo, Y.K.3
-
68
-
-
79959784751
-
Maintenance-free soft sensor models with time difference of process variables
-
Kaneko, H.; Funatsu, K. Maintenance-free soft sensor models with time difference of process variables Chemom. Intell. Lab. Syst. 2011, 107, 312-317 10.1016/j.chemolab.2011.04.016
-
(2011)
Chemom. Intell. Lab. Syst.
, vol.107
, pp. 312-317
-
-
Kaneko, H.1
Funatsu, K.2
-
69
-
-
80055094175
-
A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy
-
Kaneko, H.; Funatsu, K. A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy Chemom. Intell. Lab. Syst. 2011, 109, 197-206 10.1016/j.chemolab.2011.09.003
-
(2011)
Chemom. Intell. Lab. Syst.
, vol.109
, pp. 197-206
-
-
Kaneko, H.1
Funatsu, K.2
-
70
-
-
84889677253
-
Database monitoring index for adaptive soft sensors and the application to industrial process
-
Kaneko, H.; Funatsu, K. Database monitoring index for adaptive soft sensors and the application to industrial process AIChE J. 2014, 60, 160-169 10.1002/aic.14260
-
(2014)
AIChE J.
, vol.60
, pp. 160-169
-
-
Kaneko, H.1
Funatsu, K.2
-
71
-
-
84899843065
-
A unified recursive just-in-time approach with industrial near infrared spectroscopy application
-
Chen, M.; Khare, S.; Huang, B. A unified recursive just-in-time approach with industrial near infrared spectroscopy application Chemom. Intell. Lab. Syst. 2014, 135, 133-140 10.1016/j.chemolab.2014.04.007
-
(2014)
Chemom. Intell. Lab. Syst.
, vol.135
, pp. 133-140
-
-
Chen, M.1
Khare, S.2
Huang, B.3
-
72
-
-
34147222905
-
On-line soft sensor for polyethylene process with multiple production grades
-
Liu, J. On-line soft sensor for polyethylene process with multiple production grades Control Engineering Practice 2007, 15, 769-778 10.1016/j.conengprac.2005.12.005
-
(2007)
Control Engineering Practice
, vol.15
, pp. 769-778
-
-
Liu, J.1
-
73
-
-
84891520527
-
Novel just-in-time learning-based soft sensor utilizing non-Gaussian information
-
Xie, L.; Zeng, J.; Gao, C. Novel just-in-time learning-based soft sensor utilizing non-Gaussian information IEEE Transactions on Control Systems Technology 2014, 22, 360-368 10.1109/TCST.2013.2248155
-
(2014)
IEEE Transactions on Control Systems Technology
, vol.22
, pp. 360-368
-
-
Xie, L.1
Zeng, J.2
Gao, C.3
-
75
-
-
73849104985
-
An ensemble ELM based on modified AdaBoost. RT algorithm for predicting the temperature of molten steel in ladle furnace
-
Tian, H.-X.; Mao, Z.-Z. An ensemble ELM based on modified AdaBoost. RT algorithm for predicting the temperature of molten steel in ladle furnace IEEE Transactions on Automation Science and Engineering 2010, 7, 73-80 10.1109/TASE.2008.2005640
-
(2010)
IEEE Transactions on Automation Science and Engineering
, vol.7
, pp. 73-80
-
-
Tian, H.-X.1
Mao, Z.-Z.2
-
76
-
-
84888306466
-
Design of inferential sensors in the process industry: A review of Bayesian methods
-
Khatibisepehr, S.; Huang, B.; Khare, S. Design of inferential sensors in the process industry: A review of Bayesian methods J. Process Control 2013, 23, 1575-1596 10.1016/j.jprocont.2013.05.007
-
(2013)
J. Process Control
, vol.23
, pp. 1575-1596
-
-
Khatibisepehr, S.1
Huang, B.2
Khare, S.3
-
77
-
-
84949117585
-
A dynamic and on-line ensemble regression for changing environments
-
Soares, S. G.; Araújo, R. A dynamic and on-line ensemble regression for changing environments Expert Systems with Applications 2015, 42, 2935-2948 10.1016/j.eswa.2014.11.053
-
(2015)
Expert Systems with Applications
, vol.42
, pp. 2935-2948
-
-
Soares, S.G.1
Araújo, R.2
-
78
-
-
84910607774
-
An on-line weighted ensemble of regressor models to handle concept drifts
-
Gomes Soares, S.; Araújo, R. An on-line weighted ensemble of regressor models to handle concept drifts Engineering Applications of Artificial Intelligence 2015, 37, 392-406 10.1016/j.engappai.2014.10.003
-
(2015)
Engineering Applications of Artificial Intelligence
, vol.37
, pp. 392-406
-
-
Gomes Soares, S.1
Araújo, R.2
-
79
-
-
79955611348
-
Applicability domains and accuracy of prediction of soft sensor models
-
Kaneko, H.; Arakawa, M.; Funatsu, K. Applicability domains and accuracy of prediction of soft sensor models AIChE J. 2011, 57, 1506-1513 10.1002/aic.12351
-
(2011)
AIChE J.
, vol.57
, pp. 1506-1513
-
-
Kaneko, H.1
Arakawa, M.2
Funatsu, K.3
-
80
-
-
84883613892
-
Estimation of predictive accuracy of soft sensor models based on data density
-
Kaneko, H.; Funatsu, K. Estimation of predictive accuracy of soft sensor models based on data density Chemom. Intell. Lab. Syst. 2013, 128, 111-117 10.1016/j.chemolab.2013.08.005
-
(2013)
Chemom. Intell. Lab. Syst.
, vol.128
, pp. 111-117
-
-
Kaneko, H.1
Funatsu, K.2
-
82
-
-
84919797914
-
Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development
-
Fan, M.; Ge, Z.; Song, Z. Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development Ind. Eng. Chem. Res. 2014, 53, 19979-19986 10.1021/ie5029864
-
(2014)
Ind. Eng. Chem. Res.
, vol.53
, pp. 19979-19986
-
-
Fan, M.1
Ge, Z.2
Song, Z.3
-
83
-
-
84859911625
-
Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models
-
Xie, X.; Shi, H. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models Ind. Eng. Chem. Res. 2012, 51, 5497-5505 10.1021/ie202720y
-
(2012)
Ind. Eng. Chem. Res.
, vol.51
, pp. 5497-5505
-
-
Xie, X.1
Shi, H.2
-
84
-
-
33947702137
-
A sliding-window kernel RLS algorithm and its application to nonlinear channel identification
-
Toulouse, France, May 14-19
-
Van Vaerenbergh, S.; Via, J.; Santamaría, I. A sliding-window kernel RLS algorithm and its application to nonlinear channel identification. 2006 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 14-19, 2006; pp V-V.
-
(2006)
2006 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
, pp. 5-5
-
-
Van Vaerenbergh, S.1
Via, J.2
Santamaría, I.3
-
87
-
-
0037110983
-
A modular simulation package for fed-batch fermentation: Penicillin production
-
Birol, G.; Ündey, C.; Cinar, A. A modular simulation package for fed-batch fermentation: penicillin production Comput. Chem. Eng. 2002, 26, 1553-1565 10.1016/S0098-1354(02)00127-8
-
(2002)
Comput. Chem. Eng.
, vol.26
, pp. 1553-1565
-
-
Birol, G.1
Ündey, C.2
Cinar, A.3
-
88
-
-
34547147425
-
Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data
-
Zhao, C.; Wang, F.; Gao, F.; Lu, N.; Jia, M. Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data Ind. Eng. Chem. Res. 2007, 46, 4943-4953 10.1021/ie061320f
-
(2007)
Ind. Eng. Chem. Res.
, vol.46
, pp. 4943-4953
-
-
Zhao, C.1
Wang, F.2
Gao, F.3
Lu, N.4
Jia, M.5
-
89
-
-
44349144443
-
Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data
-
Zhao, C.; Wang, F.; Mao, Z.; Lu, N.; Jia, M. Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data Ind. Eng. Chem. Res. 2008, 47, 3104-3113 10.1021/ie701680y
-
(2008)
Ind. Eng. Chem. Res.
, vol.47
, pp. 3104-3113
-
-
Zhao, C.1
Wang, F.2
Mao, Z.3
Lu, N.4
Jia, M.5
-
90
-
-
58149308461
-
Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction
-
Zhao, C.; Wang, F.; Gao, F. Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction Chemom. Intell. Lab. Syst. 2009, 95, 107-121 10.1016/j.chemolab.2008.09.003
-
(2009)
Chemom. Intell. Lab. Syst.
, vol.95
, pp. 107-121
-
-
Zhao, C.1
Wang, F.2
Gao, F.3
-
91
-
-
84875048235
-
Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares
-
Hu, Y.; Ma, H.; Shi, H. Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares Chemom. Intell. Lab. Syst. 2013, 123, 15-27 10.1016/j.chemolab.2013.02.004
-
(2013)
Chemom. Intell. Lab. Syst.
, vol.123
, pp. 15-27
-
-
Hu, Y.1
Ma, H.2
Shi, H.3
-
92
-
-
84919445476
-
Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process
-
Jin, H.; Chen, X.; Yang, J.; Wu, L.; Wang, L. Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process ISA Trans. 2014, 53, 1822-1837 10.1016/j.isatra.2014.08.015
-
(2014)
ISA Trans.
, vol.53
, pp. 1822-1837
-
-
Jin, H.1
Chen, X.2
Yang, J.3
Wu, L.4
Wang, L.5
|