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Rough Sets in the Neuro-Fuzzy Architectures Based on Monotonic Fuzzy Implications

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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

In this paper we presented a general solution to compose rough-neuro-fuzzy architectures. Monotonic properties of fuzzy implications were assumed to derive fuzzy systems in the case of missing features. The fuzzy implications satisfying Fodor’s lemma used in logical approach and t-norms used in Mamdani approach are discussed.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czȩstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czȩstochowa, POLAND

    Robert Nowicki

  2. Department of Artificial Inteligence The Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905r. 64, 90-222, Łódź, POLAND

    Robert Nowicki

Authors
  1. Robert Nowicki
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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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Nowicki, R. (2004). Rough Sets in the Neuro-Fuzzy Architectures Based on Monotonic Fuzzy Implications. 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_76

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_76

  • 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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