On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Connectives and quantifiers in fuzzy sets
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Generalized Fuzzy Aggregation Operators
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Generalized Fuzzy Aggregation Operators
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Learning in Pattern Recognition
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
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Fuzzy logic offers the option to try to model the non-linearity of the functioning of the human brain when several pieces of evidence are combined to make an inference. In the proposed scheme a Fuzzy reasoning system includes a training stage during which the most appropriate aggregation operators are selected. To allow for different importance to be given to different pieces of evidence, the Fuzzy membership functions used are allowed to take values in a range [0; w], with w ≠ 1. Then the need arises for the generalization of the aggregetion operators to cope with such membership functions. In this paper we examine the properties of such generalised operators, that make them appropriate for use in Fuzzy reasoning.