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Novel Methods of FCM Model Reduction

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Computational Intelligence and Mathematics for Tackling Complex Problems 2

Part of the book series: Studies in Computational Intelligence ((SCI,volume 955))

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Abstract

Fuzzy Cognitive Maps are widely applied to support decision making tasks. It is often hard for experts to create the model of a system that provides the required accuracy but simple enough to easily use in practice. In general, it is better to create complex models first, because they can be computationally reduced later until they preserve the required accuracy but become simple enough. Two novel Fuzzy Cognitive Map reduction methods based on K-Means and Fuzzy C-Means clustering are suggested in order to generate simplified models that hopefully mimic the behavior of the original model better than the already existing methods. After the quick overview of the existing techniques found in literature, a simple and a complex model of a real-life problem are reduced to varying degrees with the suggested new methods and with an existing one. The first results of the comparison are published in this paper, too.

The paper was written with the support of the project titled “Internationalisation, initiatives to establish a new source of researchers and graduates and development of knowledge and technological transfer as instruments of intelligent specialisations at Széchenyi István University” (project number: EFOP-3.6.1-16-2016-00017). M. F. H. acknowledges the financial support of the DE Excellence Program. L. T. K. is supported by NKFIH K108405 and K124055 grants.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Széchenyi István University, Győr, Hungary

    Miklós F. Hatwágner & László T. Kóczy

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

    László T. Kóczy

Authors
  1. Miklós F. Hatwágner
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  2. László T. Kóczy
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Corresponding author

Correspondence to Miklós F. Hatwágner .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Department of Mathematics, University of Cádiz, Cádiz, Spain

    María Eugenia Cornejo

  2. Faculty of Engineering Sciences, Széchenyi István University, Gyõr, Hungary

    László T. Kóczy

  3. Science Faculty, Department of Mathematics, University of Cádiz, Cádiz, Spain

    Jesús Medina-Moreno

  4. Escuela de Ingeniería Industrial, Universidad de Castilla La Mancha, Toledo, Spain

    Juan Moreno-García

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Hatwágner, M.F., Kóczy, L.T. (2022). Novel Methods of FCM Model Reduction. In: Cornejo, M.E., Kóczy, L.T., Medina-Moreno, J., Moreno-García, J. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems 2. Studies in Computational Intelligence, vol 955. Springer, Cham. https://doi.org/10.1007/978-3-030-88817-6_12

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