Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection / Yu Miao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Titre : Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection Type de document : texte imprimé Auteurs : Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur Année de publication : 2010 Article en page(s) : pp. 10903–10911 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Support--Vector--Regression--Approach--Simultaneous--Data--Reconciliation--Gross Error--Outlier Detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f [article] Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection [texte imprimé] / Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur . - 2010 . - pp. 10903–10911. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Mots-clés : Support--Vector--Regression--Approach--Simultaneous--Data--Reconciliation--Gross Error--Outlier Detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f

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Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection / Yu Miao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Titre : Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection Type de document : texte imprimé Auteurs : Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur Année de publication : 2010 Article en page(s) : pp. 10903–10911 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Support vector regression Outlier detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f [article] Support vector regression approach for simultaneous data reconciliation and gross error or outlier detection [texte imprimé] / Yu Miao, Auteur ; Hongye Su, Auteur ; Ouguan Xu, Auteur . - 2010 . - pp. 10903–10911. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10903–10911 Mots-clés : Support vector regression Outlier detection Résumé : Process data measurements are important for model fitting, process monitoring, control, optimization, and management decision making, and they are usually applied with parameter estimation. This paper applies the support vector (SV) regression approach as a framework for simultaneous data reconciliation and gross error or outlier detection in processes. SV regression minimizes regularized risk instead of maximum likelihood, and it is a compromise between empirical risk and model complexity. For data reconciliation, it is robust to random and gross errors or outliers, because loss functions are addressed as objective functions instead of least-squares. Furthermore, because of the robust nature of SV regressions, the coefficients of the SV regression itself have less effect on the reconciled results. Finally, a number of process and literature system simulation results show the features of the SV regression approach for data reconciliation proposed in this paper. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801629f

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