We consider here fuzzy quantities, i.e., fuzzy sets without any hypothesis about normality nor convexity. Two main topics are examined: the first one consists in defining the evaluation of a fuzzy quantity, in such a way that it may be applied both in ranking and in defuzzification problems. The definition is based on α-cuts and depends on two parameters: a coefficient connected with the optimistic or pessimistic attitude of the decision maker and a weighting function similar to a density function. The second aim is showing that the proposed definition is suitable for defuzzifying the output of a fuzzy expert system: we treat a classical example discussed in [2], using several t-norms and t-conorms in aggregation procedures.

A general defuzzification method for a fuzzy system output depending on different T-norms

FACCHINETTI, Gisella;
2009-01-01

Abstract

We consider here fuzzy quantities, i.e., fuzzy sets without any hypothesis about normality nor convexity. Two main topics are examined: the first one consists in defining the evaluation of a fuzzy quantity, in such a way that it may be applied both in ranking and in defuzzification problems. The definition is based on α-cuts and depends on two parameters: a coefficient connected with the optimistic or pessimistic attitude of the decision maker and a weighting function similar to a density function. The second aim is showing that the proposed definition is suitable for defuzzifying the output of a fuzzy expert system: we treat a classical example discussed in [2], using several t-norms and t-conorms in aggregation procedures.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/347234
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