in this paper, a new method is proposed to find the fuzzy optimal solution of fully fuzzy linear programming (abbreviated to fflp) problems. also, we employ linear programming (lp) with equality constraints to find a nonegative fuzzy number vector x which satisfies ax =b, where a is a fuzzy number matrix. then we investigate the existence of a positive solution of fully fuzzy linear system (ffls).

there are different methods of reconstructing hydrologic data. depending on the conditions of the station a particular method can produce the best results. generally, in order to estimate the lost data in a station and its surrounding stations, hydrologic, climatologic and/or physiolographic similarities are used. recently, the fuzzy regression method has been used to reconstract the hydrologic...

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the Lagrangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the crossvalidation procedure for selecting the o...

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.

in this paper, a numerical method for nding minimal solution of a mn fullyfuzzy linear system of the form ax = b based on pseudo inverse calculation,is given when the central matrix of coecients is row full rank or column fullrank, and where a~ is a non-negative fuzzy mn matrix, the unknown vectorx is a vector consisting of n non-negative fuzzy numbers and the constant b isa vector consisti...

In this paper, a revisited approach for fuzzy regression linear model representation and identification is introduced. By adopting the commonly used principle of D-cuts, the fuzzy regression implementation is reduced to the handling of conventional intervals, for inputs, parameters and outputs. Using the Midpoint-Radius representation of intervals, the uncertainty attached to linear models beco...

An algorithm for real-time prediction of river stage dynamics using a Takagi-Sugeno fuzzy system is presented in this paper. The system is trained incrementally each time step and is used to predict onestep and multi-step ahead of river stages. The number of input variables that were considered in the analysis was determined using two statistical methods, i.e. autocorrelation and partial autoco...

Upper and lower regression models (dual possibilistic models) are proposed for data analysis with crisp inputs and interval or fuzzy outputs. Based on the given data, the dual possibilistic models can be derived from upper and lower directions, respectively, where the inclusion relationship between these two models holds. Thus, the inherent uncertainty existing in the given phenomenon can be ap...

Logistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situa...