Unification of Imprecise Data: Translation of Fuzzy to Multi-Valued Knowledge Over Y-Axis

Soumaya Moussa, Saoussen Bel Hadj Kacem and Moncef Tagina
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Soumaya Moussa: LARIA, National School of Computer Sciences, University of Manouba, Tunisia
Saoussen Bel Hadj Kacem: Faculty of Economic Sciences and Management of Nabeul, University of Carthage, Tunisia
Moncef Tagina: LARIA, National School of Computer Sciences, University of Manouba, Tunisia

International Journal of Fuzzy System Applications (IJFSA), 2022, vol. 11, issue 1, 1-27

Abstract: Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.

Date: 2022
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