Documents disponibles écrits par cet auteur

Combining kernel partial least - squares modeling and iterative learning control for the batch - to - batch optimization of constrained nonlinear processes / Yingwei Zhang in Industrial & engineering chemistry research, Vol. 49 N° 16 (Août 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 16 (Août 2010) . - pp. 7470–7477 Titre : Combining kernel partial least - squares modeling and iterative learning control for the batch - to - batch optimization of constrained nonlinear processes Type de document : texte imprimé Auteurs : Yingwei Zhang, Auteur ; Yunpeng Fan, Auteur ; Pengchao Zhang, Auteur Année de publication : 2010 Article en page(s) : pp. 7470–7477 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Non  linear  system  Optimization  Batchwise  Learning  Modeling  Partial  least  squares Résumé : A new approach to the optimal control with constraints is proposed to achieve a desired end product quality, and a modified kernel partial least-squares (KPLS) is used to build the combining model of nonlinear processes. The particle swarm optimization algorithm is used to solve the optimal problem. The contributions of the article are as follows: The modified KPLS is proposed for the optimal control purpose, and the optimal manipulated variables are computed for the next batch run based on modified KPLS. The proposed approach is applied to a bulk polymerization of styrene batch process and fused magnesium furnace. Simulation results show the proposed approach is effective for predicting the control profile of next batch run. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23109320 [article] Combining kernel partial least - squares modeling and iterative learning control for the batch - to - batch optimization of constrained nonlinear processes [texte imprimé] / Yingwei Zhang, Auteur ; Yunpeng Fan, Auteur ; Pengchao Zhang, Auteur . - 2010 . - pp. 7470–7477. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 16 (Août 2010) . - pp. 7470–7477 Mots-clés : Non  linear  system  Optimization  Batchwise  Learning  Modeling  Partial  least  squares Résumé : A new approach to the optimal control with constraints is proposed to achieve a desired end product quality, and a modified kernel partial least-squares (KPLS) is used to build the combining model of nonlinear processes. The particle swarm optimization algorithm is used to solve the optimal problem. The contributions of the article are as follows: The modified KPLS is proposed for the optimal control purpose, and the optimal manipulated variables are computed for the next batch run based on modified KPLS. The proposed approach is applied to a bulk polymerization of styrene batch process and fused magnesium furnace. Simulation results show the proposed approach is effective for predicting the control profile of next batch run. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23109320

Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM) / Yingwei Zhang in Industrial & engineering chemistry research, Vol. 47 N°18 (Septembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°18 (Septembre 2008) . - p. 6961–6971 Titre : Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM) Type de document : texte imprimé Auteurs : Yingwei Zhang, Auteur Année de publication : 2008 Article en page(s) : p. 6961–6971 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Kernel  independent  component  analysis  Fault  detection Résumé : In this article, the nonlinear dynamic process monitoring method based on kernel independent component analysis (KICA) is developed. Compared to the Support Vector Machine (SVM) method, KICA is unsupervised and available for fault detection. Hence, in this article, KICA is used to detect faults. Because the dimension of the feature space is far less than the rank of kernel matrix, a basis in feature space is selected. Specifically, the basis in feature space is first constructed based on the similarity factor of data in one group in this article. A contribution plot is impossible, because the nonlinear mapping function from input space into feature space is unknown. Therefore, KICA is difficult for nonlinear fault diagnosis. In this article, once a fault is detected, the kernel-transformed scores from improved KICA will be directly introduced as the inputs of SVM to diagnose the fault. The classification rate of SVM plus improved KICA is higher than the classification rate of SVM plus KICA when the same number of independent components (nICs) is selected. The reason is that the negentropy in improved KICA plus SVM could take into account the more-useful information of original inputs than that of original KICA plus SVM. The training time of SVM plus improved KICA is shorter than that of SVM plus KICA, because the former attenuates the expensive computation load. The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process and a wastewater treatment process (WWTP). Applications indicate that the proposed approach effectively captures the nonlinear dynamic in the process variables and shows superior fault detectability, compared to conventional methods. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071496x [article] Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM) [texte imprimé] / Yingwei Zhang, Auteur . - 2008 . - p. 6961–6971. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°18 (Septembre 2008) . - p. 6961–6971 Mots-clés : Kernel  independent  component  analysis  Fault  detection Résumé : In this article, the nonlinear dynamic process monitoring method based on kernel independent component analysis (KICA) is developed. Compared to the Support Vector Machine (SVM) method, KICA is unsupervised and available for fault detection. Hence, in this article, KICA is used to detect faults. Because the dimension of the feature space is far less than the rank of kernel matrix, a basis in feature space is selected. Specifically, the basis in feature space is first constructed based on the similarity factor of data in one group in this article. A contribution plot is impossible, because the nonlinear mapping function from input space into feature space is unknown. Therefore, KICA is difficult for nonlinear fault diagnosis. In this article, once a fault is detected, the kernel-transformed scores from improved KICA will be directly introduced as the inputs of SVM to diagnose the fault. The classification rate of SVM plus improved KICA is higher than the classification rate of SVM plus KICA when the same number of independent components (nICs) is selected. The reason is that the negentropy in improved KICA plus SVM could take into account the more-useful information of original inputs than that of original KICA plus SVM. The training time of SVM plus improved KICA is shorter than that of SVM plus KICA, because the former attenuates the expensive computation load. The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process and a wastewater treatment process (WWTP). Applications indicate that the proposed approach effectively captures the nonlinear dynamic in the process variables and shows superior fault detectability, compared to conventional methods. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071496x