Documents disponibles écrits par cet auteur

Localized, adaptive recursive partial least squares regression for dynamic system modeling / Wangdong Ni in Industrial & engineering chemistry research, Vol. 51 N° 23 (Juin 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 23 (Juin 2012) . - pp. 8025-8039 Titre : Localized, adaptive recursive partial least squares regression for dynamic system modeling Type de document : texte imprimé Auteurs : Wangdong Ni, Auteur ; Soon Keat Tan, Auteur ; Wun Jern Ng, Auteur Année de publication : 2012 Article en page(s) : pp. 8025-8039 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Modeling  Partial  least  squares Résumé : A localized and adaptive recursive partial least squares algorithm (LARPLS), based on the local learning framework, is presented in this paper. The algorithm is used to address, among other issues in the recursive partial least-squares (RPLS) regression algorithm, the "forgetting factor" and sensitivity of variable scaling. Two levels of local adaptation, namely, (1) local model adaptation and (2) local time regions adaptation, and three adaptive strategies, (a) means and variances adaptation, (b) adaptive forgetting factor, and (c) adaptive extraction of local time regions, are provided using the LARPLS algorithm. Compared to RPLS, the LARPLS model is proven to be more adaptive in the face of process change, maintaining superior predictive performance, as demonstrated in the modeling of three different types of processes. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25990315 [article] Localized, adaptive recursive partial least squares regression for dynamic system modeling [texte imprimé] / Wangdong Ni, Auteur ; Soon Keat Tan, Auteur ; Wun Jern Ng, Auteur . - 2012 . - pp. 8025-8039. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 23 (Juin 2012) . - pp. 8025-8039 Mots-clés : Modeling  Partial  least  squares Résumé : A localized and adaptive recursive partial least squares algorithm (LARPLS), based on the local learning framework, is presented in this paper. The algorithm is used to address, among other issues in the recursive partial least-squares (RPLS) regression algorithm, the "forgetting factor" and sensitivity of variable scaling. Two levels of local adaptation, namely, (1) local model adaptation and (2) local time regions adaptation, and three adaptive strategies, (a) means and variances adaptation, (b) adaptive forgetting factor, and (c) adaptive extraction of local time regions, are provided using the LARPLS algorithm. Compared to RPLS, the LARPLS model is proven to be more adaptive in the face of process change, maintaining superior predictive performance, as demonstrated in the modeling of three different types of processes. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25990315

Moving - window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing / Wangdong Ni in Industrial & engineering chemistry research, Vol. 51 N° 18 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 18 (Mai 2012) . - pp. 6416-6428 Titre : Moving - window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing Type de document : texte imprimé Auteurs : Wangdong Ni, Auteur ; Soon Keat Tan, Auteur ; Wun Jern Ng, Auteur Année de publication : 2012 Article en page(s) : pp. 6416-6428 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Updating  Modeling Résumé : The characteristics of nonlinearity and time-varying changes in most industrial processes usually cripple the predictive performance of conventional soft sensors. In this article, moving-window Gaussian process regression (MWGPR) is proposed to effectively capture the process dynamics and to model nonlinearity simultaneously. Applications of the proposed MWGPR method to the modeling of the activity of a catalyst and an industrial propylene polymerization process are presented. The results clearly demonstrate that the MWGPR method effectively tracks the process changes to generate satisfactory predictive performance. Two modeling strategies, namely, dual updating and dual preprocessing, are applied to MWGPR, in an attempt to more efficiently track the process dynamics. Dual updating takes into account both the time-varying variance of the process and the bias between the actual measurement and the model prediction. The improvement in performance is illustrated by a case study on modeling catalyst activity. Simultaneous removal of embedded noise in both process parameters and process output variables by dual preprocessing could significantly improve the predictive capability of MWGPR, as illustrated by the performance of a modeling study of an industrial propylene polymerization process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25867293 [article] Moving - window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing [texte imprimé] / Wangdong Ni, Auteur ; Soon Keat Tan, Auteur ; Wun Jern Ng, Auteur . - 2012 . - pp. 6416-6428. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 18 (Mai 2012) . - pp. 6416-6428 Mots-clés : Updating  Modeling Résumé : The characteristics of nonlinearity and time-varying changes in most industrial processes usually cripple the predictive performance of conventional soft sensors. In this article, moving-window Gaussian process regression (MWGPR) is proposed to effectively capture the process dynamics and to model nonlinearity simultaneously. Applications of the proposed MWGPR method to the modeling of the activity of a catalyst and an industrial propylene polymerization process are presented. The results clearly demonstrate that the MWGPR method effectively tracks the process changes to generate satisfactory predictive performance. Two modeling strategies, namely, dual updating and dual preprocessing, are applied to MWGPR, in an attempt to more efficiently track the process dynamics. Dual updating takes into account both the time-varying variance of the process and the bias between the actual measurement and the model prediction. The improvement in performance is illustrated by a case study on modeling catalyst activity. Simultaneous removal of embedded noise in both process parameters and process output variables by dual preprocessing could significantly improve the predictive capability of MWGPR, as illustrated by the performance of a modeling study of an industrial propylene polymerization process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25867293