fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.

soil conservation service (scs) adjusted the kostiakovs infiltration model by adding a constant coefficient, for improvement of estimation. the improved model has three constant parameters, which are difficult to calculate. so scs has taken the third constant parameter as equal to 0.65-0.7 cm to simplify the estimation. this parameter varies in different soil and often outranges the scs estimat...