메뉴 건너뛰기




Volumn 53, Issue 51, 2014, Pages 19979-19986

Adaptive gaussian mixture model-based relevant sample selection for JITL soft sensor development

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; NUMERICAL METHODS;

EID: 84919797914     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie5029864     Document Type: Conference Paper
Times cited : (53)

References (28)
  • 1
    • 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 (4) 795-814
    • (2009) Comput. Chem. Eng. , vol.33 , Issue.4 , pp. 795-814
    • Kadlec, P.1    Gabrys, B.2    Strandt, S.3
  • 2
    • 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) 1-24
    • (2011) Comput. Chem. Eng. , vol.35 , Issue.1 , pp. 1-24
    • Kadlec, P.1    Grbić, R.2    Gabrys, B.3
  • 4
    • 0001681052 scopus 로고
    • The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses
    • Wold, S.; Ruhe, A.; Wold, H.; Dunn, W. J., III. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses SIAM J. Sci. Stat. Comput. 1984, 5 (3) 735-743
    • (1984) SIAM J. Sci. Stat. Comput. , vol.5 , Issue.3 , pp. 735-743
    • Wold, S.1    Ruhe, A.2    Wold, H.3    Dunn, W.J.4
  • 5
    • 57049112694 scopus 로고    scopus 로고
    • 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 (1) 43-49
    • (2009) Comput. Chem. Eng. , vol.33 , Issue.1 , pp. 43-49
    • Gonzaga, J.1    Meleiro, L.2    Kiang, C.3    Maciel Filho, R.4
  • 6
    • 27444433806 scopus 로고    scopus 로고
    • 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 (3) 225-239
    • (2006) Biochem. Eng. J. , vol.27 , Issue.3 , pp. 225-239
    • Desai, K.1    Badhe, Y.2    Tambe, S.S.3    Kulkarni, B.D.4
  • 8
    • 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 (4) 503-514
    • (1998) Comput. Chem. Eng. , vol.22 , Issue.4 , pp. 503-514
    • Joe Qin, S.1
  • 9
    • 79955915214 scopus 로고    scopus 로고
    • Real-time fault diagnosis approach based on lifting wavelet and recursive LSSVM
    • Qing, Y.; Feng, T.; Dazhi, W.; Dongsheng, W.; Anna, W. Real-time fault diagnosis approach based on lifting wavelet and recursive LSSVM Chin. J. Sci. Instrum. 2011, 32 (3) 596-602
    • (2011) Chin. J. Sci. Instrum. , vol.32 , Issue.3 , pp. 596-602
    • Qing, Y.1    Feng, T.2    Dazhi, W.3    Dongsheng, W.4    Anna, W.5
  • 10
    • 84879309312 scopus 로고    scopus 로고
    • 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 (7) 2339-2347
    • (2013) AIChE J. , vol.59 , Issue.7 , pp. 2339-2347
    • Kaneko, H.1    Funatsu, K.2
  • 11
    • 2942558590 scopus 로고    scopus 로고
    • 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 (13) 2801-2810
    • (2004) Chem. Eng. Sci. , vol.59 , Issue.13 , pp. 2801-2810
    • Cheng, C.1    Chiu, M.-S.2
  • 12
    • 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 (2) 306-317
    • (2010) Chemom. Intell. Lab. Syst. , vol.104 , Issue.2 , pp. 306-317
    • Ge, Z.1    Song, Z.2
  • 13
    • 84863278909 scopus 로고    scopus 로고
    • Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor
    • Liu, Y.; Huang, D.; Li, Y. Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor Ind. Eng. Chem. Res. 2012, 51 (8) 3356-3367
    • (2012) Ind. Eng. Chem. Res. , vol.51 , Issue.8 , pp. 3356-3367
    • Liu, Y.1    Huang, D.2    Li, Y.3
  • 14
    • 50649095932 scopus 로고    scopus 로고
    • Online monitoring of nonlinear multiple mode processes based on adaptive local model approach
    • Ge, Z.; Song, Z. Online monitoring of nonlinear multiple mode processes based on adaptive local model approach Control Eng. Pract. 2008, 16 (12) 1427-1437
    • (2008) Control Eng. Pract. , vol.16 , Issue.12 , pp. 1427-1437
    • Ge, Z.1    Song, Z.2
  • 15
    • 68049143320 scopus 로고    scopus 로고
    • 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 (7) 1754-1765
    • (2009) AIChE J. , vol.55 , Issue.7 , pp. 1754-1765
    • Fujiwara, K.1    Kano, M.2    Hasebe, S.3    Takinami, A.4
  • 16
  • 17
    • 84906872234 scopus 로고    scopus 로고
    • 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 (35) 13736-13749
    • (2014) Ind. Eng. Chem. Res. , vol.53 , Issue.35 , pp. 13736-13749
    • Yuan, X.1    Ge, Z.2    Song, Z.3
  • 18
    • 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) 1-13
    • (2005) Chemom. Intell. Lab. Syst. , vol.76 , Issue.1 , pp. 1-13
    • Cheng, C.1    Chiu, M.-S.2
  • 19
    • 0030287048 scopus 로고    scopus 로고
    • The expectation-maximization algorithm
    • Moon, T. K. The expectation-maximization algorithm Signal Process. Mag., IEEE 1996, 13 (6) 47-60
    • (1996) Signal Process. Mag., IEEE , vol.13 , Issue.6 , pp. 47-60
    • Moon, T.K.1
  • 21
    • 0001441372 scopus 로고
    • Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks
    • MacKay, D. J. Probable networks and plausible predictions-a review of practical Bayesian methods for supervised neural networks Network: Comput. Neural Syst. 1995, 6 (3) 469-505
    • (1995) Network: Comput. Neural Syst. , vol.6 , Issue.3 , pp. 469-505
    • Mackay, D.J.1
  • 24
    • 0035998835 scopus 로고    scopus 로고
    • Model-based clustering, discriminant analysis, and density estimation
    • Fraley, C.; Raftery, A. E. Model-based clustering, discriminant analysis, and density estimation J. Am. Stat. Assoc. 2002, 97 (458) 611-631
    • (2002) J. Am. Stat. Assoc. , vol.97 , Issue.458 , pp. 611-631
    • Fraley, C.1    Raftery, A.E.2
  • 28
    • 33846516584 scopus 로고    scopus 로고
    • Information Science and Statistics Series; Springer-Verlag: New York
    • Bishop, C. M. Pattern Recognition and Machine Learning; Information Science and Statistics Series; Springer-Verlag: New York, 2006.
    • (2006) Pattern Recognition and Machine Learning
    • Bishop, C.M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.