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Confidence Estimation of GMDH Neural Networks

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

This paper presents a new parameter and confidence estimation techniques for static GMDH neural networks. The main objective is to show how to employ the outer-bounding ellipsoid algorithm to solve such a challenging task that occurs in many practical situations. In particular, the proposed approach can be relatively easy applied in robust fault diagnosis schemes.

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References

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65–246, Zielona Góra, Poland

    Józef Korbicz, Mihai F. Metenidis, Marcin Mrugalski & Marcin Witczak

Authors
  1. Józef Korbicz
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  2. Mihai F. Metenidis
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  3. Marcin Mrugalski
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  4. Marcin Witczak
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Editor information

Editors and Affiliations

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

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

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

    Ryszard Tadeusiewicz

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

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Korbicz, J., Metenidis, M.F., Mrugalski, M., Witczak, M. (2004). Confidence Estimation of GMDH Neural Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_27

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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