This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by...

Regression analysis refers to methods by which estimates are made for the model parameters from knowledge of values a given input-output data set. The aim this research is find suitable and determine ‘best’ data. In statistical regression analysis, deviations between observed output corresponding predicted attributed random errors. It often assumed that distribution these errors Gaussian. On ot...

Where fuzzy regression can be applied and, in which conditions fuzzy regression method more appropriate tool for the investigations are identified in this paper. The contrast between fuzzy regression and ordinary regression analysis and three approach of fuzzy regression are summarized. Key-Words: fuzzy regression, vaguness, fuzziness, randomness, insufficient data, assumptions.

permeability prediction problem has been examined using several methods such as empirical formulas, regression analysis and intelligent systems especially neural networks and fuzzy logic. this study proposes an improved and novel model for predicting permeability from conventional well log data. the methodology is integration of empirical formulas, multiple regression and neuro-fuzzy in a commi...

We propose a functional extension of fuzzy clusterwise regression, which estimates fuzzy memberships of clusters and regression coefficient functions for each cluster simultaneously. The proposedmethod permits dependent and/or predictor variables to be functional, varying over time, space, and other continua. The fuzzy memberships and clusterwise regression coefficient functions are estimated b...