메뉴 건너뛰기




Volumn 58, Issue , 2013, Pages 288-297

Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size

Author keywords

Degradation; Hyperparameter; Online support vector machine; Process control; Soft sensor; Time variable

Indexed keywords

ADAPTIVE SOFT-SENSOR; HYPER-PARAMETER; ONLINE SUPPORT VECTOR MACHINES; PREDICTIVE ACCURACY; SOFT SENSOR MODELS; SOFT SENSORS; TIME VARIABLE; TIME-VARYING CHANGES;

EID: 84883140452     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2013.07.016     Document Type: Article
Times cited : (36)

References (31)
  • 2
    • 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. Chemical Engineering Science 2004, 59:2801-2810.
    • (2004) Chemical Engineering Science , vol.59 , pp. 2801-2810
    • Cheng, C.1    Chiu, M.S.2
  • 3
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • Cherkassky V., Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004, 17:113-126.
    • (2004) Neural Networks , vol.17 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 5
    • 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 Journal 2009, 55:1754-1765.
    • (2009) AIChE Journal , vol.55 , pp. 1754-1765
    • Fujiwara, K.1    Kano, M.2    Hasebe, S.3    Takinami, A.4
  • 6
    • 33846087392 scopus 로고    scopus 로고
    • Online trained support vector machines-based generalized predictive control of non-linear systems
    • Iplikci S. Online trained support vector machines-based generalized predictive control of non-linear systems. International Journal of Adaptive Control and Signal Processing 2006, 20:599-621.
    • (2006) International Journal of Adaptive Control and Signal Processing , vol.20 , pp. 599-621
    • Iplikci, S.1
  • 7
    • 79954599740 scopus 로고    scopus 로고
    • 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 Journal 2010, 57:1288-1301.
    • (2010) AIChE Journal , vol.57 , pp. 1288-1301
    • Kadlec, P.1    Gabrys, B.2
  • 10
    • 58449118276 scopus 로고    scopus 로고
    • 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 Journal 2009, 55:87-98.
    • (2009) AIChE Journal , vol.55 , pp. 87-98
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 11
    • 79955476246 scopus 로고    scopus 로고
    • Novel soft sensor method for detecting completion of transition in industrial polymer processes
    • Kaneko H., Arakawa M., Funatsu K. Novel soft sensor method for detecting completion of transition in industrial polymer processes. Computers & Chemical Engineering 2011, 35:1135-1142.
    • (2011) Computers & Chemical Engineering , vol.35 , pp. 1135-1142
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 12
    • 79959784751 scopus 로고    scopus 로고
    • 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. Chemometrics and Intelligent Laboratory Systems 2011, 107:312-317.
    • (2011) Chemometrics and Intelligent Laboratory Systems , vol.107 , pp. 312-317
    • Kaneko, H.1    Funatsu, K.2
  • 13
    • 80055094175 scopus 로고    scopus 로고
    • 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. Chemometrics and Intelligent Laboratory Systems 2011, 109:197-206.
    • (2011) Chemometrics and Intelligent Laboratory Systems , vol.109 , pp. 197-206
    • Kaneko, H.1    Funatsu, K.2
  • 14
    • 80052838846 scopus 로고    scopus 로고
    • Development of soft sensor models based on time difference of process variables with accounting for nonlinear relationship
    • Kaneko H., Funatsu K. Development of soft sensor models based on time difference of process variables with accounting for nonlinear relationship. Industrial and Engineering Chemistry Research 2011, 50:10643-10651.
    • (2011) Industrial and Engineering Chemistry Research , vol.50 , pp. 10643-10651
    • Kaneko, H.1    Funatsu, K.2
  • 15
    • 84875311589 scopus 로고    scopus 로고
    • Automatic determination method based on cross-validation for optimal intervals of time difference
    • Kaneko H., Funatsu K. Automatic determination method based on cross-validation for optimal intervals of time difference. Journal of Chemical Engineering of Japan 2013, 46:1-7.
    • (2013) Journal of Chemical Engineering of Japan , vol.46 , pp. 1-7
    • Kaneko, H.1    Funatsu, K.2
  • 16
    • 84872918863 scopus 로고    scopus 로고
    • Discussion on time difference models and intervals of time difference for application of soft sensors
    • Kaneko H., Funatsu K. Discussion on time difference models and intervals of time difference for application of soft sensors. Industrial and Engineering Chemistry Research 2013, 52:1322-1334.
    • (2013) Industrial and Engineering Chemistry Research , vol.52 , pp. 1322-1334
    • Kaneko, H.1    Funatsu, K.2
  • 17
    • 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 Journal 2013, 59:2339-2347.
    • (2013) AIChE Journal , vol.59 , pp. 2339-2347
    • Kaneko, H.1    Funatsu, K.2
  • 18
    • 35548968908 scopus 로고    scopus 로고
    • 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. Computers & Chemical Engineering 2008, 32:12-24.
    • (2008) Computers & Chemical Engineering , vol.32 , pp. 12-24
    • Kano, M.1    Nakagawa, Y.2
  • 19
    • 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. International Journal of Pharmaceutics 2011, 421:269-274.
    • (2011) International Journal of Pharmaceutics , vol.421 , pp. 269-274
    • Kim, S.1    Kano, M.2    Nakagawa, H.3    Hasebe, S.4
  • 20
    • 72149085992 scopus 로고    scopus 로고
    • An accumulative error based adaptive design of experiments for offline metamodeling
    • Li G., Aute V., Azarm S. An accumulative error based adaptive design of experiments for offline metamodeling. Structural and Multidisciplinary Optimization 2010, 40:137-155.
    • (2010) Structural and Multidisciplinary Optimization , vol.40 , pp. 137-155
    • Li, G.1    Aute, V.2    Azarm, S.3
  • 21
    • 0141765796 scopus 로고    scopus 로고
    • Accurate on-line support vector regression
    • Ma J., Theliler J., Perkins S. Accurate on-line support vector regression. Neural Computation 2003, 15:2683-2703.
    • (2003) Neural Computation , vol.15 , pp. 2683-2703
    • Ma, J.1    Theliler, J.2    Perkins, S.3
  • 22
    • 0026169941 scopus 로고
    • On-line inference of polymer properties in an industrial polyethylene reactor
    • McAuley K.B., MacGregor J.F. On-line inference of polymer properties in an industrial polyethylene reactor. AIChE Journal 1991, 37:825-835.
    • (1991) AIChE Journal , vol.37 , pp. 825-835
    • McAuley, K.B.1    MacGregor, J.F.2
  • 23
    • 58349104545 scopus 로고    scopus 로고
    • Online - SVR for short-term traffic flow prediction under typical and atypical traffic conditions
    • Neto M.C., Jeong Y.S., Jeong M.K., Han L.D. Online - SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications 2009, 36:6164-6173.
    • (2009) Expert Systems with Applications , vol.36 , pp. 6164-6173
    • Neto, M.C.1    Jeong, Y.S.2    Jeong, M.K.3    Han, L.D.4
  • 24
    • 11144244974 scopus 로고    scopus 로고
    • Prediction of pellet properties for an industrial bimodal high-density polyethylene process with Ziegler-Natta catalysts
    • Oh S.J., Lee J., Park S. Prediction of pellet properties for an industrial bimodal high-density polyethylene process with Ziegler-Natta catalysts. Industrial and Engineering Chemistry Research 2005, 44:8-20.
    • (2005) Industrial and Engineering Chemistry Research , vol.44 , pp. 8-20
    • Oh, S.J.1    Lee, J.2    Park, S.3
  • 25
  • 27
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data modelling
    • Qin S.J. Recursive PLS algorithms for adaptive data modelling. Computers & Chemical Engineering 1998, 22:503-514.
    • (1998) Computers & Chemical Engineering , vol.22 , pp. 503-514
    • Qin, S.J.1
  • 28
    • 0036639869 scopus 로고    scopus 로고
    • Scalable techniques from on parametric statistics for real time robot learning
    • Schaal S., Atkeson C.G., Vijayakumar S. Scalable techniques from on parametric statistics for real time robot learning. Applied Intelligence 2002, 17:49-60.
    • (2002) Applied Intelligence , vol.17 , pp. 49-60
    • Schaal, S.1    Atkeson, C.G.2    Vijayakumar, S.3
  • 29
    • 84868224530 scopus 로고    scopus 로고
    • 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. Industrial and Engineering Chemistry Research 2012, 51:13227-13237.
    • (2012) Industrial and Engineering Chemistry Research , vol.51 , pp. 13227-13237
    • Yu, J.1
  • 30
    • 84859392648 scopus 로고    scopus 로고
    • 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. Computers & Chemical Engineering 2012, 41:134-144.
    • (2012) Computers & Chemical Engineering , vol.41 , pp. 134-144
    • Yu, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.