Traditional statistical tools for performing regression cannot handle the large uncertainties present in a fuzzy number dataset. Therefore we will have to rely on techniques developed for interval analysis (Moore 1966; Moore 1979). A useful property of algorithms that handle fuzzy numbers is that they can be rewritten in terms of intervals allowing the powerful mathematical tools of interval an...

In this paper, we focus on multiobjective fuzzy random programming problems with simple recourse through a fractile optimization model, in which fuzzy random variables coefficients are involved in equality constraints, and random variables coefficients are involved in the objective functions. In the proposed method, equality constraints with fuzzy random variables are defined on the basis of a ...

in structural time seriesregression models, binary (classic) dummy variables are used to quantify the effects of economic structural breaks, changes or qualitative variables. the binary dummy variable can be challenged using this dummy variable in which 0 or 1 represent absence or presence of structural breaks(or changes), but presence of the structural breaks(or changes) may not have uniform e...

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...

Fuzzy regression models have been applied to operational research (OR) applications such as forecasting. Some of previous studies on fuzzy regression analysis obtain crisp regression coefficients for eliminating the problem of increasing spreads for the estimated fuzzy responses as the magnitude of the independent variable increases; however, they still cannot cope with the situation of decreas...

In this paper, we propose a scalar variable formation of fuzzy regression model based on the axiomatic credibility measure foundation. The fuzzy estimation for fuzzy regression coefficients is investigated. A general M-estimation criterion is developed under Maximum Fuzzy Uncertainty Principle, which resulted in weighted Normal equation with adjusted term for M-estimator of the regression coeff...

In this paper, a new method for estimating the linear regression coefficients approximation is presented based on Z-numbers. In this model, observations are real numbers, regression coefficients and dependent variables (y) have values ​​for Z-numbers. To estimate the coefficients of this model, we first convert the linear regression model based on Z-numbers into two fuzzy linear regression mode...

In reliability, quality control and risk analysis, fuzzy methodologies are more and more involved and inevitably introduced difficulties in seeking fuzzy functional relationship between factors. In this paper, we propose a scalar variable formation of fuzzy regression model based on the credibility measure theoretical foundation. It is expecting our scalar variable treatments on fuzzy regressio...

Quadratic programming (QP) is an optimization problem wherein one minimizes (or maximizes) a quadratic function of a finite number of decision variable subject to a finite number of linear inequality and/ or equality constraints. In this paper, a quadratic programming problem (FFQP) is considered in which all cost coefficients, constraints coefficients, and right hand side are characterized by ...