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Nonlinear PLS integrated with error-based LSSVM and its application to NOx modeling / You Lv in Industrial & engineering chemistry research, Vol. 51 N° 49 (Décembre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 49 (Décembre 2012) . - pp. 16092-16100 Titre : Nonlinear PLS integrated with error-based LSSVM and its application to NOx modeling Type de document : texte imprimé Auteurs : You Lv, Auteur ; Jizhen Liu, Auteur ; Tingting Yang, Auteur Année de publication : 2013 Article en page(s) : pp. 16092-16100 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Modeling Résumé : This article presents a novel nonlinear partial least-squares (PLS) method to address a nonlinear problem with input collinearity in an industrial process. The proposed method integrates an inner least-squares support vector machine (LSSVM) function with an outer linear PLS framework. First, the input and output latent variables are extracted to eliminate the collinearity through PLS projection, and then LSSVM is used to construct nonlinear relation between each pair of latent variables. Moreover, a weight-updating procedure is incorporated to enhance the accuracy of prediction. Then, training and predicting algorithms based on modified nonlinear iterative partial least-squares (NIPALS) steps are also described in detail. The performance of the new method is also investigated with a benchmark data set. Finally, this approach is applied to a real industrial process to predict the NOx emissions of a coal-fired boiler. The root-mean-square errors (RMSEs) on the training and testing data decreased to only 12.6632 and 37.6609, respectively. Compared with the original linear PLS and other kinds of nonlinear PLS methods, a reduction of approximately 40.8―47.4% in the prediction errors is attained. The results reveal that the new approach is capable of modeling the nonlinear relation of NOx emissions with other process parameters and improving the prediction performance. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26732135 [article] Nonlinear PLS integrated with error-based LSSVM and its application to NOx modeling [texte imprimé] / You Lv, Auteur ; Jizhen Liu, Auteur ; Tingting Yang, Auteur . - 2013 . - pp. 16092-16100. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 49 (Décembre 2012) . - pp. 16092-16100 Mots-clés : Modeling Résumé : This article presents a novel nonlinear partial least-squares (PLS) method to address a nonlinear problem with input collinearity in an industrial process. The proposed method integrates an inner least-squares support vector machine (LSSVM) function with an outer linear PLS framework. First, the input and output latent variables are extracted to eliminate the collinearity through PLS projection, and then LSSVM is used to construct nonlinear relation between each pair of latent variables. Moreover, a weight-updating procedure is incorporated to enhance the accuracy of prediction. Then, training and predicting algorithms based on modified nonlinear iterative partial least-squares (NIPALS) steps are also described in detail. The performance of the new method is also investigated with a benchmark data set. Finally, this approach is applied to a real industrial process to predict the NOx emissions of a coal-fired boiler. The root-mean-square errors (RMSEs) on the training and testing data decreased to only 12.6632 and 37.6609, respectively. Compared with the original linear PLS and other kinds of nonlinear PLS methods, a reduction of approximately 40.8―47.4% in the prediction errors is attained. The results reveal that the new approach is capable of modeling the nonlinear relation of NOx emissions with other process parameters and improving the prediction performance. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26732135