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The Extended Hierarchical Linguistic Model in Fuzzy Cognitive Maps

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Abstract

Fuzzy cognitive maps allow multi-expert causality modelling using linguistic 2-tuples values to improve the accuracy of the computing with words processes regarding classical symbolic approaches. Experts provide causal relations according to their knowledge, because they can have different educational backgrounds, or experiences. It seems logical that they might use different scales to express their mental models. In this work, we propose a new method for extending fuzzy cognitive maps, using the computing with words paradigm and the extended hierarchical linguistic model making it possible to model causal relation by means of linguistic information, where experts would use different linguistic scales to express causal relations. An illustrative example is shown to demonstrate the applicability of the proposed method in the modelling of interdependencies among nonfunctional requirements.

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Correspondence to Maikel Leyva-Vázquez .

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Leyva-Vázquez, M., Santos-Baquerizo, E., Peña-González, M., Cevallos-Torres, L., Guijarro-Rodríguez, A. (2016). The Extended Hierarchical Linguistic Model in Fuzzy Cognitive Maps. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., del Cioppo, J., Vera-Lucio, N. (eds) Technologies and Innovation. CITI 2016. Communications in Computer and Information Science, vol 658. Springer, Cham. https://doi.org/10.1007/978-3-319-48024-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-48024-4_4

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