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 associative aggregators and 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, decision making, classification image processing, idea this work is continue previous studies related derived from general overlap In order obtain more flexible context, we extend class implications by negations t-norms, replacing latter functions, obtaining so-called (GO,N)-implication We also investigate their properties, aggregation characterization intersections with classes