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Monte Carlo Based Detection of Parameter Correlation in Simulation Models

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Mechatronics 2019: Recent Advances Towards Industry 4.0 (MECHATRONICS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1044))

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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.

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Authors and Affiliations

  1. MECHLAB, ISMMB, FME, Brno University of Technology, Brno, Czech Republic

    Jan Najman, Martin Brablc, Matej Rajchl, Michal Bastl, Tomáš Spáčil & Martin Appel

Authors
  1. Jan Najman
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  2. Martin Brablc
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  3. Matej Rajchl
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  4. Michal Bastl
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  5. Tomáš Spáčil
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  6. Martin Appel
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Corresponding author

Correspondence to Jan Najman .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw, Poland

    Roman Szewczyk

  2. Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Jiří Krejsa

  3. Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw, Poland

    Michał Nowicki

  4. Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw, Poland

    Anna Ostaszewska-Liżewska

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Najman, J., Brablc, M., Rajchl, M., Bastl, M., Spáčil, T., Appel, M. (2020). Monte Carlo Based Detection of Parameter Correlation in Simulation Models. In: Szewczyk, R., Krejsa, J., Nowicki, M., Ostaszewska-Liżewska, A. (eds) Mechatronics 2019: Recent Advances Towards Industry 4.0. MECHATRONICS 2019. Advances in Intelligent Systems and Computing, vol 1044. Springer, Cham. https://doi.org/10.1007/978-3-030-29993-4_7

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