A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals

作者：Bisserier Amory; Boukezzoula Reda^*; Galichet Sylvie

来源：Information Sciences, 2010, 180(19): 3653-3673.

DOI：10.1016/j.ins.2010.06.017

• Fuzzy linear regression
• Fuzzy intervals
• Total inclusion
• Model identification

Conventional Fuzzy regression using possibilistic concepts allows the identification of models from uncertain data sets. However, some limitations still exist. This paper deals with a revisited approach for possibilistic fuzzy regression methods. Indeed, a new modified fuzzy linear model form is introduced where the identified model output can envelop all the observed data and ensure a total inclusion property. Moreover, this model output can have any kind of spread tendency. In this framework, the identification problem is reformulated according to a new criterion that assesses the model fuzziness independently from the collected data distribution. The potential of the proposed method with regard to the conventional approach is illustrated by simulation examples.

• 出版日期2010-10-1