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Designing of State-Space Neural Model and Its Application to Robust Fault Detection

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

This paper presents a new methodology of designing of non-linear dynamic neural model in the state-space representation. Furthermore, an application of the Unscented Kalman Filter to the training of the designed neural model is also shown. The final part of this work provides an illustrative example of the application of the proposed methodology to the identification and robust fault detection of the tunnel furnace.

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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

    Marcin Mrugalski

Authors
  1. Marcin Mrugalski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2013 Springer-Verlag Berlin Heidelberg

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Mrugalski, M. (2013). Designing of State-Space Neural Model and Its Application to Robust Fault Detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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