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Deterministic Annealing Integrated with ε-Insensitive Learning in Neuro-fuzzy Systems

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

In this paper a new method of parameters estimation for neuro-fuzzy system with parameterized consequents is presented. The novelty of the learning algorithm consists of an application of the deterministic annealing method integrated with ε-insensitive learning. This method allows to improve neuro-fuzzy modeling quality in the sense of an increase in generalization ability and outliers robustness. To demonstrate performance of the proposed procedure two numerical experiments concerning benchmark problems of prediction and identification are given.

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Author information

Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, ul Akademicka 16, 44-101, Gliwice, Poland

    Robert Czabański

Authors
  1. Robert Czabański
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Czabański, R. (2006). Deterministic Annealing Integrated with ε-Insensitive Learning in Neuro-fuzzy Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_24

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  • DOI: https://doi.org/10.1007/11785231_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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