Knowledge based systems need to deal with aggregation and fusion of data with uncertainty. To use many sources of information in numerical forms for the purpose of decision or conclusion, systems suppose to have tools able to represent the knowledge in a mathematical form. One of the solutions is to use fuzzy logic operators. We present in this article an improvement of the triple Π operator introduced by Yager and Rybalov, which is calledmean3Π. Whereas triple Π is an operator completely reinforced, the presented operator is a mean operator, which makes it more robust to noise.
Recent developments of sensors and computers have raised the problem of handling huge amounts of complex data that users try to synthesize for decision making. Aggregation operators, such as those appearing in fuzzy sets theory, are useful tools for this synthesis but in their present formulation, these operators only deal with a finite set of arguments. In this paper, we introduce G3Π, an extension of both Yager–Rybalov Triple Π and Mean Triple Π operators to general measure spaces that can deal with temporal or spatiotemporal intensive data streams. Known properties and inequalities are extended in this more general setting. The notion of moving G3Π is also introduced and it can be applied to a solar radiation data stream. This may lead to further works on data fusion and on similar extensions of some other operators.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.