In performing a fuzzy multiple linear regression model, important topics are: to measure the fitting quality of the model and to find the “best” set of input variables that explain the variation in the observed system responses. In this paper, by considering an exploratory approach, to express the quality of fit of a fuzzy linear regression model, a coefficient of multiple determination R2 for ...

The software tools are described that are designed for multivariate linear regression analysis of fuzzy data. The underlying method of f-regression is thoroughly described in [4]. The tools can be used for modeling both linear relationships and relationships that are nonlinear by nature, but linear in the parameters. Source data can be both crisp and fuzzy. The solution of a technical problem i...

aim : to determine the associated factors of reflux duration, using quantile regression model. background : reflux is one of the most prevalent gastrointestinal disorders.  statistical tools are widely used to assess the associated factors on reflux severity and frequency. patients and methods : a door to door questionnaire has been used to evaluate gastrointestinal symptoms including reflux an...

This paper explores the use of fuzzy membership values generated by fuzzy c-means clustering (FCM) method to predict soil properties over space. A weighted average model was used on fuzzy membership to get soil properties. To validate the efficiency of this model, it was then compared with a multiple linear regression model between the soil property and terrain attributes. Four indices were cal...

Hierarchical fuzzy modeling techniques have great advantage since model accuracy and complexity can be easily controlled thanks to the transparent model structures. A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling. In a special case (c = 2), fuzzy c-regression clustering can...

A generalized simple linear regression statistical/probabilistic model in which both input and output data can be fuzzy subsets of Rp is dealt with. The regression model is based on a fuzzy-arithmetic approach and it considers the possibility of fuzzy-valued random errors. Specifically, the least-squares estimation problem in terms of a versatile metric is addressed. The solutions are establish...