1 research outputs found
On fuzzy implications derived from general overlap functions and their relation to other classes
There are distinct techniques to generate fuzzy implication functions. Despite most of them
using the combination of associative aggregators and fuzzy negations, other connectives such as
(general) overlap/grouping functions may be a better strategy. Since these possibly non-associative
operators have been successfully used in many applications, such as decision making, classification
and image processing, the idea of this work is to continue previous studies related to fuzzy implication
functions derived from general overlap functions. In order to obtain a more general and flexible
context, we extend the class of implications derived by fuzzy negations and t-norms, replacing the
latter by general overlap functions, obtaining the so-called (GO, N)-implication functions. We also
investigate their properties, the aggregation of (GO, N)-implication functions, their characterization
and the intersections with other classes of fuzzy implication functions.This research was funded by CNPq (grant numbers: 312053/2018-5, 301618/2019-4, 311429/2020-3), FAPERGS (grant number: 19/2551-0001660-3), CAPES-Print (grant number: 88887.363001/2019-00), Spanish Ministry Science and Tech. (grant numbers: TIN2016-77356-P, PID2019-108392GB I00 (AEI/10.13039/501100011033)), and Fundación “La Caixa” (grant number: LCF/PR/PR13/51080004)