Detail publikace
Monte carlo based detection of parameter correlation in simulation models
NAJMAN, J. BRABLC, M. RAJCHL, M. BASTL, M. SPÁČIL, T. APPEL, M.
Anglický název
Monte carlo based detection of parameter correlation in simulation models
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
en
Originální abstrakt
Simulation models which are of high order or are automatically generated via modelling software are usually depended on high number of unknown parameters. In this paper we present a method for detecting correlation between these parameters and identifying the subspace shape for their uncorrelated complements. This can be further used to lower the order of the optimization problem. For our low-order examples the methods’ operating principle is visualized and the subspace is shown.
Anglický abstrakt
Simulation models which are of high order or are automatically generated via modelling software are usually depended on high number of unknown parameters. In this paper we present a method for detecting correlation between these parameters and identifying the subspace shape for their uncorrelated complements. This can be further used to lower the order of the optimization problem. For our low-order examples the methods’ operating principle is visualized and the subspace is shown.
Klíčová slova anglicky
MATLAB;Model-Based Design;Monte Carlo;Parameter correlation;Parameter estimation;PCA;Simulation;Simulink;
Vydáno
16.08.2019
Nakladatel
Springer Verlag
Místo
Warsaw
ISBN
9783030299927
ISSN
2194-5357
Kniha
Advances in Intelligent Systems and Computing - Mechatronics 2019: Recent Advances Towards Industry 4.0
Ročník
1044
Číslo edice
1044
Strany od–do
54–61
Počet stran
8
BIBTEX
@inproceedings{BUT160010,
author="Jan {Najman} and Martin {Brablc} and Matej {Rajchl} and Michal {Bastl} and Tomáš {Spáčil} and Martin {Appel},
title="Monte carlo based detection of parameter correlation in simulation models",
booktitle="Advances in Intelligent Systems and Computing - Mechatronics 2019: Recent Advances Towards Industry 4.0",
year="2019",
volume="1044",
month="August",
pages="54--61",
publisher="Springer Verlag",
address="Warsaw",
isbn="9783030299927",
issn="2194-5357"
}