Generalised Fuzzy Aggregation Operators
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.
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