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Fuzzy Selection Mechanism for Multimodel Prediction

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3214))

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Abstract

The problem of multimodel prediction of multivariate time series is considered. A fuzzy method for selection of the best model and combining of predictions is proposed in batch and recurrent forms. The method allows to estimate degrees of membership of the current state of a process to the modes described by different models.

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Author information

Authors and Affiliations

  1. Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, 14 Lenin av., Kharkiv, 61166, Ukraine

    Y. Bodyanskiy & S. Popov

Authors
  1. Y. Bodyanskiy
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  2. S. Popov
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Bodyanskiy, Y., Popov, S. (2004). Fuzzy Selection Mechanism for Multimodel Prediction. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_101

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  • DOI: https://doi.org/10.1007/978-3-540-30133-2_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23206-3

  • Online ISBN: 978-3-540-30133-2

  • eBook Packages: Springer Book Archive

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