دکتر محسن عارفی

In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance topresent the robust fuzzy model in the presence of different typesof outliers. Using some simulated data sets and some real datasets, the application of the proposed approach in modeling somecharacteristics with outliers, is studied.

A new approach to define the similarity measure between interval-valued fuzzy sets is presented. The proposed approach is based on a weighted measure in which the normalized similarities between lower functions and also between upper functions arecombined by a weight parameter. The properties of this similarity measure are investigated. It is shown that, the proposed measure has some advantages in comparison with the commonly used similarity measures.