Comparative Analysis of Symbolic Reasoning Models for Fuzzy Cognitive Maps

Mabel Frias*, Yaima Filiberto, Gonzalo Nápoles, Rafael Falcon, Rafael Bello, Koen Vanhoof

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review


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.
Original languageEnglish
Title of host publicationUncertainty Management with Fuzzy and Rough Sets: Recent Advances and Applications
EditorsRafael Bello, Rafael Falcon, José Luis Verdegay
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages13
ISBN (Print)978-3-030-10463-4
Publication statusPublished - 2019
Externally publishedYes


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