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Rough Sets in Knowledge Discovery 2 pp 449–470Cite as

Rough Sets and Artificial Neural Networks

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 19))

Abstract

This work is an attempt to summarize several approaches aimed at connecting Rough Set Theory with Artificial Neural Networks. Both methodologies have their place among intelligent classification and decision support methods. Artificial Neural Networks belong to most commonly used techniques in applications of Artificial Intelligence. During the last twenty years of its development numerous theoretical and applied works have been done in that field. Rough Set Theory which emerged about fifteen years ago is nowadays rapidly developing branch of AI and Soft Computing.

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Author information

Authors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Marcin S. Szczuka

Authors
  1. Marcin S. Szczuka
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Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University of Technology, Pl. Politechniki 1, 00-665, Warsaw, Poland

    Lech Polkowski

  2. Polish-Japanese Institute of Computer Techniques, Koszykowa 86, 02-008, Warsaw, Poland

    Lech Polkowski

  3. Institute of Mathematics, Warsaw University, ul. Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 1998 Springer-Verlag Berlin Heidelberg

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Szczuka, M.S. (1998). Rough Sets and Artificial Neural Networks. In: Polkowski, L., Skowron, A. (eds) Rough Sets in Knowledge Discovery 2. Studies in Fuzziness and Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1883-3_23

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  • DOI: https://doi.org/10.1007/978-3-7908-1883-3_23

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2459-9

  • Online ISBN: 978-3-7908-1883-3

  • eBook Packages: Springer Book Archive

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