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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
