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Constrained Bayesian state estimation using a cell filter / Sridhar Ungarala in Industrial & engineering chemistry research, Vol. 47 N°19 (Octobre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°19 (Octobre 2008) . - p. 7312–7322 Titre : Constrained Bayesian state estimation using a cell filter Type de document : texte imprimé Auteurs : Sridhar Ungarala, Auteur ; Keyu Li, Auteur ; Zhongzhou Chen, Auteur Année de publication : 2008 Article en page(s) : p. 7312–7322 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Nonlinear/non-Gaussian  processes  Constrained  cell  filter Résumé : Constrained state estimation in nonlinear/non-Gaussian processes has been the domain of optimization based methods such as moving horizon estimation (MHE). MHE has a Bayesian interpretation, but it is not practical to implement a recursive MHE without assumptions of Gaussianity and linearized dynamics at various stages. This paper presents the constrained cell filter (CCF) as an alternative to MHE, requiring no linearization, jacobians, or nonlinear program. The CCF computes a piecewise constant approximation of the state probability density function with support defined by constraints; thus, all point estimates are constrained. The CCF can be more accurate and orders of magnitude faster than MHE for problems of a size as investigated in this work. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070249q [article] Constrained Bayesian state estimation using a cell filter [texte imprimé] / Sridhar Ungarala, Auteur ; Keyu Li, Auteur ; Zhongzhou Chen, Auteur . - 2008 . - p. 7312–7322. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°19 (Octobre 2008) . - p. 7312–7322 Mots-clés : Nonlinear/non-Gaussian  processes  Constrained  cell  filter Résumé : Constrained state estimation in nonlinear/non-Gaussian processes has been the domain of optimization based methods such as moving horizon estimation (MHE). MHE has a Bayesian interpretation, but it is not practical to implement a recursive MHE without assumptions of Gaussianity and linearized dynamics at various stages. This paper presents the constrained cell filter (CCF) as an alternative to MHE, requiring no linearization, jacobians, or nonlinear program. The CCF computes a piecewise constant approximation of the state probability density function with support defined by constraints; thus, all point estimates are constrained. The CCF can be more accurate and orders of magnitude faster than MHE for problems of a size as investigated in this work. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie070249q