Publication detail

Monte carlo based detection of parameter correlation in simulation models

NAJMAN, J. BRABLC, M. RAJCHL, M. BASTL, M. SPÁČIL, T. APPEL, M.

Original Title

Monte carlo based detection of parameter correlation in simulation models

English Title

Monte carlo based detection of parameter correlation in simulation models

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

MATLAB;Model-Based Design;Monte Carlo;Parameter correlation;Parameter estimation;PCA;Simulation;Simulink;

Released

16.08.2019

Publisher

Springer Verlag

Location

Warsaw

ISBN

9783030299927

Book

Advances in Intelligent Systems and Computing - Mechatronics 2019: Recent Advances Towards Industry 4.0

Edition

1

Edition number

1044

Pages from

54

Pages to

61

Pages count

520

URL

Documents

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",
  annote="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.",
  address="Springer Verlag",
  booktitle="Advances in Intelligent Systems and Computing - Mechatronics 2019: Recent Advances Towards Industry 4.0",
  chapter="160010",
  doi="10.1007/978-3-030-29993-4_7",
  edition="1",
  howpublished="online",
  institution="Springer Verlag",
  year="2019",
  month="august",
  pages="54--61",
  publisher="Springer Verlag",
  type="conference paper"
}