Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
A new look at fuzzy connectives
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Van Melle's combining function in MYCIN is a representable uninorm: an alternative proof
Fuzzy Sets and Systems - Special issue on triangular norms
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
The functional equations of Frank and Alsina for uninorms and nullnorms
Fuzzy Sets and Systems
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Fuzzy modeling based on generalized conjunction operations
IEEE Transactions on Fuzzy Systems
Reducing the effort in the creation of new patients using the virtual simulated patient framework
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
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The problem of aggregating information represented by fuzzy sets in a meaningful way has been of central interest since the late 1970s. In most cases, the aggregation operators are defined on a pure axiomatic basis and are interpreted either as logical connectives (such as t-norms and t-conorms) or as averaging operators allowing a compensation effect (such as the arithmetic mean). On the other hand, it can be observed by some empirical tests that the above-mentioned classes of operators differ from those ones that people use in practice. Therefore, it is important to find operators that are, in a sense, mixtures of the previous ones, and allow some degree of compensation. This paper summarizes the research results of the authors that have been carried out in recent years on generalization of conventional aggregation operators. This includes, but is not limited to, the class of uninorms and nullnorms, absorbing norms, distance- and entropy-based operators, quasi-conjunctions and nonstrict means.