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Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos

In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analy...

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Detalles Bibliográficos
Autor principal: Janya-anurak, Chettapong (auth)
Formato: Electrónico Capítulo de libro
Lenguaje:inglés
Publicado: KIT Scientific Publishing 2017
Colección:Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
Materias:
ParameterschätzungUncertainty Quantification
Parameter estimation
verteilt-parametrische Systeme
Sensitivity Analysis
generalized polynomial chaos
Distributed Parameter Systems
Sensitivitätsanalyse
Unsicherheit Quantifizierung
Acceso en línea:DOAB: download the publication
DOAB: description of the publication
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Descripción
Sumario:In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.
Descripción Física:1 electronic resource (XIX, 210 p. p.)
ISBN:KSP/1000066940
9783731506423
Acceso:Open Access

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