Study on the use of uninorm aggregation operators in linguistic fuzzy modeling

This work aims to develop a practical study of the uninorms as rule antecedent aggregation operator family in Linguistic Fuzzy Modeling. Although they are well known from a theoretical point of view, they have only recently been introduced in a few specific and recent applications. Uninorms are parameterized operators that combine the membership values in the antecedent in a more flexible way than the classically employed tnorms. Therefore, we carried out an in-depth experimental study with six of them, using 23 regression problems of different size and complexity, and reached some conclusions.