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Estimation of Parameters of the Empirically Reconstructed Fuzzy Model of Measurements

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6743))

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Abstract

In this paper we introduce a method for the empirical reconstruction of a fuzzy model of measurements on the basis of testing measurements using a possibility-theoretical approach. The method of measurement reduction is developed for solving a problem of an estimation of parameters of a fuzzy system. It is shown that such problems are reduced to minimax problems. If the model is unknown it can be restored from testing experiments and can be applied for handling the problems of the type of forecasting the behavior of a system.

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Authors and Affiliations

  1. Department of Computer Methods of Physics, Faculty of Physics, Moscow State University, Moscow, 119991, Russia

    Tatiana Kopit & Alexey Chulichkov

Authors
  1. Tatiana Kopit
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  2. Alexey Chulichkov
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Editor information

Editors and Affiliations

  1. State University Higher School of Economics, Pokrovskiy Bd. 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

  2. Institute of Mathematics, University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland

    Dominik Ślęzak

  3. Department of Computer Science, University of Regina, 3737 Wascana Parkway, S4S 0A2, Regina, SK, Canada

    Daryl H. Hepting

  4. Birbeck University of London, Malet Street, WC1E 7HX, London, UK

    Boris G. Mirkin

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© 2011 Springer-Verlag Berlin Heidelberg

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Kopit, T., Chulichkov, A. (2011). Estimation of Parameters of the Empirically Reconstructed Fuzzy Model of Measurements. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-21881-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21880-4

  • Online ISBN: 978-3-642-21881-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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