Journal
INFORMATION SCIENCES
Volume 180, Issue 19, Pages 3653-3673Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.06.017
Keywords
Fuzzy linear regression; Fuzzy intervals; Total inclusion; Model identification
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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. (C) 2010 Elsevier Inc. All rights reserved.
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