Abstract
A pivotal difference between Artificial Neural Networks and Fuzzy Cognitive Maps (FCMs) is that the latter allow modeling a physical system in terms of concepts and causal relations, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by domain experts. In this paper, we explore a reasoning mechanism for FCMs based on the Computing with Words paradigm where numerical concepts and relations are replaced with linguistic terms. More explicitly, we include triangular fuzzy numbers into the qualitative reasoning process attached to our model, thus proving further interpretability and transparency. The simulations show the potential behind the symbolic reasoning mechanism proposed in this study.
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Acknowledgment
The authors would like to thank Isel Grau (Vrije Universiteit Brussel, Belgium) for her valuable suggestions on the transfer function design.
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Frias, M., Filiberto, Y., Nápoles, G., García-Socarrás, Y., Vanhoof, K., Bello, R. (2018). Fuzzy Cognitive Maps Reasoning with Words Based on Triangular Fuzzy Numbers. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_16
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DOI: https://doi.org/10.1007/978-3-030-02837-4_16
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