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Regression Modeling with Fuzzy Relations

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Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

In the paper relational neuro-fuzzy systems are described with additional fuzzy relation connecting input and output linguistic fuzzy terms. Thanks to this the fuzzy rules have more complicated structure and can be better suited the task. Fuzzy clustering and relational equations are used to obtain the initial set of fuzzy rules and systems are then learned by the backpropagation algorithm.Simulations shows excellent performance of the modified neuro-fuzzy systems.

This work was partly supported by the Foundation for Polish Science (Professorial Grant 2005-2008) and the Polish Ministry of Science and Higher Education (Habilitation Project 2007-2010 Nr N N516 1155 33, Special Research Project 2006-2009, Polish-Singapore Research Project 2008-2010, Research Project 2008-2010).

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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

    Rafał Scherer

  2. Department of Artificial Intelligence, Academy of Humanities and Economics in Lodz, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

Authors
  1. Rafał Scherer
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Scherer, R. (2008). Regression Modeling with Fuzzy Relations. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_31

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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