Topological relations between bipolar fuzzy sets based on mathematical morphology

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Abstract

In many domains of information processing, both vagueness, or imprecision, and bipolarity, encompassing positive and negative parts of information, are core features of the information to be modeled and processed. This led to the development of the concept of bipolar fuzzy sets, and of associated models and tools. Here we propose to extend these tools by defining algebraic relations between bipolar fuzzy sets, including intersection, inclusion, adjacency and RCC relations widely used in mereotopology, based on bipolar connectives (in a logical sense) and on mathematical morphology operators.

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Bloch, I. (2017). Topological relations between bipolar fuzzy sets based on mathematical morphology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10225 LNCS, pp. 40–51). Springer Verlag. https://doi.org/10.1007/978-3-319-57240-6_4

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