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Framework for analysis and identification of nonlinear distributed parameter systems using Bayesian uncertainty quantification based on generalized polynomial chaos

 
: Janya-anurak, Chettapong
: Beyerer, Jürgen

:
Volltext urn:nbn:de:0072-669407 (16 MByte PDF)
MD5 Fingerprint: 059ac80ac3e0003f2c81726b82e08dc0
Erstellt am: 11.4.2017


Karlsruhe: KIT Scientific Publishing, 2017, XIX, 210 S.
Zugl.: Karlsruhe, Inst. für Technologie (KIT), Diss., 2017
Karlsruher Schriften zur Anthropomatik, 31
ISBN: 978-3-7315-0642-3
Englisch
Dissertation, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-441502.html