Claire-Eleuthèriane Gerrer
Phimeca Engineering, France
Sylvain Girard
Phimeca Engineering, France
Ladda ner artikel
http://dx.doi.org/10.3384/ecp19157189Ingår i: Proceedings of the 13th International Modelica Conference, Regensburg, Germany, March 4–6, 2019
Linköping Electronic Conference Proceedings 157:19, s. 8
Publicerad: 2019-02-01
ISBN: 978-91-7685-122-7
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Most advanced mathematical methods for the analysis of numerical model cannot cope with functional outputs of dynamic Modelica models. Principal component analysis is a well established method for dimension reduction, and can be used to tackle this issue. It relies however on a linear hypothesis that limits its applicability. We illustrate on a case study how the non linear method of autoassociative model overcomes this shortcoming and provides physically interpretable data representations.
dimension reduction, functional data analysis, FMI, OtFMI, principal component analysis, autoassociative model, sensitivity analysis
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