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Comparative Analysis of Symbolic Reasoning Models for Fuzzy Cognitive Maps

  • Mabel FriasEmail author
  • Yaima Filiberto
  • Gonzalo Nápoles
  • Rafael Falcon
  • Rafael Bello
  • Koen Vanhoof
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 377)

Abstract

Fuzzy Cognitive Maps (FCMs) can be defined as recurrent neural networks that allow modeling complex systems using concepts and causal relations. While this Soft Computing technique has proven to be a valuable knowledge-based tool for building Decision Support Systems, further improvements related to its transparency are still required. In this paper, we focus on designing an FCM-based model where both the causal weights and concepts’ activation values are described by words like low, medium or high. Hybridizing FCMs and the Computing with Words paradigm leads to cognitive models closer to human reasoning, making it more comprehensible for decision makers. The simulations using a well-known case study related to simulation scenarios illustrate the soundness and potential application of the proposed model.

Notes

Acknowledgements

The authors would like to thank to John T. Rickard from Distributed Infinity, Inc. Larkspur, CO, USA for his support with the simulations.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mabel Frias
    • 1
    Email author
  • Yaima Filiberto
    • 1
  • Gonzalo Nápoles
    • 2
  • Rafael Falcon
    • 3
  • Rafael Bello
    • 4
  • Koen Vanhoof
    • 2
  1. 1.Department of Computer ScienceUniversity of CamagueyCamagueyCuba
  2. 2.Hasselt Universiteit Agoralaan gebouw DDiepenbeekBelgium
  3. 3.Larus Technologies Corporation, School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  4. 4.Department of Computer ScienceUniversity of Las VillasSanta ClaraCuba

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