In this paper, a revisited approach for fuzzy regression linear model representation and identification is introduced. By adopting the commonly used principle of -cuts, the fuzzy regression implementation is reduced to the handling of conventional intervals, for inputs, parameters and outputs. Using the Midpoint-Radius rep- resentation of intervals, the uncertainty attached to linear models becomes more interpretable. Actually, it is possible to determine the output uncertainty origin (model parameters and/or inputs). In this context, a possibilistic regression method is proposed to identify models of minimal global uncertainty, that is with respect to all pos- sible inputs. Keywords Interval Regression, Fuzzy Regression, Uncertainty Representation, Fuzzy Inputs-Fuzzy Outputs.
['Amory Bisserier', 'Reda Boukezzoula', 'Sylvie Galichet']
An Interval Approach for Fuzzy Linear Regression with Imprecise Data