Information Aggregation in Intelligent Systems Using Generalized Operators

Abstract

Aggregation of information represented by membership functions is a central matter in intelligent systems where fuzzy rule base and reasoning mechanism are applied. Typical examples of such systems consist of, but not limited to, fuzzy control, decision support and expert systems. Since the advent of fuzzy sets a great number of fuzzy connectives, aggregation operators have been introduced. Some families of such operators (like t-norms) have become standard in the field. Nevertheless, it also became clear that these operators do not always follow the real phenomena. Therefore, there is a natural need for finding new operators to develop more sophisticated intelligent systems. This paper summarizes the research results of the authors that have been carried out in recent years on generalization of conventional operators.