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Fuzzy Cognitive Maps: A Business Intelligence Discussion

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Intelligent Decision Technologies 2019

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 142))

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Abstract

Modeling complex systems by means of computational models has enabled experts to understand the problem domain without the need of waiting for the real events to happen. In that regard, fuzzy cognitive maps (FCMs) have become an important tool in the neural computing field because of their flexibility and transparency. However, obtaining a model able to align its dynamical behavior with the problem domain is not always trivial. In this paper, we discuss some aspects to be considered when designing FCM-based simulation models by relying on a business intelligence case study. In a nutshell, when the fixed point is unique, we recommend to focus on the number of iterations to converge instead of focusing on the reached attractor and stress the importance of the transfer function chosen in the model.

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Acknowledgements

The authors would like to sincerely thank Prof. Dr. István Á. Harmati from the Budapest University of Technology and Economics, Hungary, for kindly revising the technical correctness of this paper.

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Authors and Affiliations

  1. Hasselt University, Hasselt, Belgium

    Gonzalo Nápoles, Greg Van Houdt, Manal Laghmouch, Wouter Goossens, Quinten Moesen & Benoît Depaire

Authors
  1. Gonzalo Nápoles
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  2. Greg Van Houdt
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  3. Manal Laghmouch
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  4. Wouter Goossens
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  5. Quinten Moesen
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  6. Benoît Depaire
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Corresponding author

Correspondence to Gonzalo Nápoles .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, UK

    Robert J. Howlett

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Nápoles, G., Van Houdt, G., Laghmouch, M., Goossens, W., Moesen, Q., Depaire, B. (2020). Fuzzy Cognitive Maps: A Business Intelligence Discussion. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-13-8311-3_8

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