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Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms

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Book cover Recent Advances in Decision Making

Part of the book series: Studies in Computational Intelligence ((SCI,volume 222))

Abstract

Fuzzy systems have been successfully used in the area of controllers for a long time. The Mamdani method is one of the most popular inference systems for practical applications. The main problem of Mamdani-type inference system and other fuzzy logic based controllers is how to gain the fuzzy rules the inference system based on. Several approaches have been proposed for automatic rule base identification. The bacterial type evolutionary algorithms have been successfully applied for solving this task. These algorithms are based on the Pseudo-Bacterial Genetic Algorithm and are supplied with operations and methods (e.g. the Levenberg-Marquardt method) to complete their task more efficiently. The goal is to create more accurate fuzzy rule bases from input-output data sets as quickly as possible. In this work, we summarize the bacterial type evolutionary algorithms used for fuzzy rule base identification.

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Author information

Authors and Affiliations

  1. Department of Automation, Széchenyi István University, Győr, Hungary

    János Botzheim

  2. Department of Telecommunication and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary

    János Botzheim & László T. Kóczy

  3. Institute of Information Technology and Electrical Engineering, Széchenyi István University, Győr, Hungary

    László Gál & László T. Kóczy

  4. Department of Technology, Informatics and Economy, University of West Hungary, Szombathely, Hungary

    László Gál

Authors
  1. János Botzheim
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  2. László Gál
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  3. László T. Kóczy
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Editor information

Editors and Affiliations

  1. School of Engineering Department of Mathematics and Science, Blekinge Institute of Technology, S-37179, Karlskrona, Sweden

    Elisabeth Rakus-Andersson

  2. Machine Intelligence Institute, Iona College, 715 N.Avenue, 10801-1890, New Rochelle, NY, USA

    Ronald R. Yager

  3. School of Electrical and Information Engineering, University of South Australia , SA 5095, South Australia, Australia

    Nikhil Ichalkaranje

  4. School of Electrical and Information Engineering, University of South Australia , Mawson Lakes Campus, SA 5095, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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© 2009 Springer-Verlag Berlin Heidelberg

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Botzheim, J., Gál, L., Kóczy, L.T. (2009). Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms. In: Rakus-Andersson, E., Yager, R.R., Ichalkaranje, N., Jain, L.C. (eds) Recent Advances in Decision Making. Studies in Computational Intelligence, vol 222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02187-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-02187-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02186-2

  • Online ISBN: 978-3-642-02187-9

  • eBook Packages: EngineeringEngineering (R0)

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