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...
- Autores:
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2017
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/17600
- Acceso en línea:
- https://directory.doabooks.org/handle/20.500.12854/47993
http://hdl.handle.net/20.500.12010/17600
- Palabra clave:
- Parameterschätzung Uncertainty Quantification
Parameter estimation
Verteilt-parametrische Systeme
Diseño de sistemas
Análisis de sistemas
Procesamiento electrónico de datos - Técnicas estructuradas
- Rights
- License
- Abierto (Texto Completo)
| Summary: | 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. |
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