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Using Weight Constraints and Masking to Improve Fuzzy Cognitive Map Models

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Book cover Creativity in Intelligent Technologies and Data Science (CIT&DS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 754))

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Abstract

The paper presents a novel supervised learning method for fuzzy cognitive maps adapted from the theory of artificial neural networks. The main objective in designing the method was to pay closer attention to the distinctions that exist between fuzzy cognitive maps, and the original model for which the method was intended – whether it was a feedforward neural network, a recurrent network, or an energy-based model. The augmented version strives to properly build upon the various strengths of fuzzy cognitive maps – particularly on their interpretability, which arises from the close coupling that exists between their nodes and particular concepts. It is shown that the augmented method is able to outperform existing approaches. Notably, the ability of the learned model to generalize correctly, and to faithfully reconstruct the original system is studied.

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Acknowledgements

This work has been supported by the Cultural and Educational Grant Agency of the Slovak Republic (KEGA) No. 038ŽU-4/2017: “Laboratory education methods of automatic identification and localization using radiofrequency identification technology”.

Author information

Authors and Affiliations

  1. Department of Control and Information Systems, Faculty of Electrical Engineering, University of Žilina, Žilina, Slovakia

    Michal Gregor

  2. Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    Peter P. Groumpos

  3. Department of Industrial Engineering, Faculty of Mechanical Engineering, University of Žilina, Žilina, Slovakia

    Milan Gregor

Authors
  1. Michal Gregor
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  2. Peter P. Groumpos
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  3. Milan Gregor
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Corresponding author

Correspondence to Michal Gregor .

Editor information

Editors and Affiliations

  1. Volgograd State Technical University, Volgograd, Russia

    Alla Kravets

  2. Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

  3. Volgograd State Technical University, Volgograd, Russia

    Marina Kultsova

  4. University of Patras, Patras, Greece

    Peter Groumpos

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Gregor, M., Groumpos, P.P., Gregor, M. (2017). Using Weight Constraints and Masking to Improve Fuzzy Cognitive Map Models. In: Kravets, A., Shcherbakov, M., Kultsova, M., Groumpos, P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2017. Communications in Computer and Information Science, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-65551-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-65551-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65550-5

  • Online ISBN: 978-3-319-65551-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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