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Symbolic Regression with the AMSTA+GP in a Non-linear Modelling of Dynamic Objects

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Artificial Intelligence and Soft Computing (ICAISC 2018)

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Abstract

In this paper, we present a new version of the State Transition Algorithm, which allows to automatically determine the number and range of local models that describe the behaviour of a non-linear dynamic object. We used this data as input for genetic programming algorithm in order to create a simple functional model of the non-linear dynamic object which is not computationally demanded and has high accuracy.

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Łukasz Bartczuk & Piotr Dziwiński

  2. Information Technology Institute, University of Social Sciences, Łódź, Poland

    Andrzej Cader

  3. Clark University, Worchester, USA

    Andrzej Cader

Authors
  1. Łukasz Bartczuk
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  2. Piotr Dziwiński
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  3. Andrzej Cader
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Corresponding author

Correspondence to Łukasz Bartczuk .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Bartczuk, Ł., Dziwiński, P., Cader, A. (2018). Symbolic Regression with the AMSTA+GP in a Non-linear Modelling of Dynamic Objects. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_45

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_45

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  • Online ISBN: 978-3-319-91262-2

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