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Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach / Tian-Hong Pan in Industrial & engineering chemistry research, Vol. 49 N° 10 (Mai 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 10 (Mai 2010) . - pp. 4738–4747 Titre : Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach Type de document : texte imprimé Auteurs : Tian-Hong Pan, Auteur ; David Shan-Hill Wong, Auteur ; Shi-Shang Jang, Auteur Année de publication : 2010 Article en page(s) : pp. 4738–4747 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Heat  Exchanger  Genetic  Algorithm Résumé : In this study, using data-driven methods, we develop a soft senor based on a multiple local model for a nonlinear industrial process. The soft sensor is based on a novel learning algorithm, which uses online subtractive clustering to recursively update the structure and parameters of a local model network. We also propose rules for updating the centers and local model coefficients of existing clusters, for generating new clusters and new models as well as for merging existing clusters and their corresponding models. As an industrial example, the proposed algorithm is applied to an o-xylene purification column, and it is shown that it is possible to track dynamic trends and compactly accumulate operating experiences. The performance of the proposed approach is compared with that of adaptive principal component regression, adaptive linear models based on key variables selection, fixed partial least-squares, and radial basic function neural network. The results demonstrate the effectiveness of the proposed modeling approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901098w [article] Development of a novel soft sensor using a local model network with an adaptive subtractive clustering approach [texte imprimé] / Tian-Hong Pan, Auteur ; David Shan-Hill Wong, Auteur ; Shi-Shang Jang, Auteur . - 2010 . - pp. 4738–4747. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 10 (Mai 2010) . - pp. 4738–4747 Mots-clés : Heat  Exchanger  Genetic  Algorithm Résumé : In this study, using data-driven methods, we develop a soft senor based on a multiple local model for a nonlinear industrial process. The soft sensor is based on a novel learning algorithm, which uses online subtractive clustering to recursively update the structure and parameters of a local model network. We also propose rules for updating the centers and local model coefficients of existing clusters, for generating new clusters and new models as well as for merging existing clusters and their corresponding models. As an industrial example, the proposed algorithm is applied to an o-xylene purification column, and it is shown that it is possible to track dynamic trends and compactly accumulate operating experiences. The performance of the proposed approach is compared with that of adaptive principal component regression, adaptive linear models based on key variables selection, fixed partial least-squares, and radial basic function neural network. The results demonstrate the effectiveness of the proposed modeling approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901098w