[[abstract]]By considering two criteria of minimum total sum of vagueness and minimum total sum of squares in estimation, this article proposes a variable selection method for a fuzzy regression equation with crisp-input and fuzzy-output. A branch-and-bound algorithm is designed and 'the least resistance principle' is adopted to determine the set of compromised solutions. Numerical examples are provided for illustration.[[fileno]]2020402010022[[department]]工工
Abstract—A novel approach is introduced to construct a fuzzy regression model when both input data a...
[[abstract]]The method for obtaining the fuzzy estimates of regression parameters with the help of &...
We present an efficient method for selecting important input variables when building a fuzzy model f...
AbstractBy considering two criteria of minimum total sum of vagueness and minimum total sum of squar...
In fuzzy domain, a variable (vague linguistic term) often depends not only on a single variable but ...
In order to estimate fuzzy regression models, possibilistic and least-squares procedures can be cons...
[[abstract]]In this study, using necessity analysis, we located a fuzzy regression interval that is ...
[[abstract]]Fuzzy linear regression was originally introduced by Tanaka et al. To cope with differen...
In the classical multiple regression modeling, there might be some insignificant input variables. Th...
Market researches and opinion polls usually include customers’ responses as verbal labels of sets wi...
Abstract change points. In addition, setting the change points to derive a piecewise fuzzy regressio...
[[abstract]]The range of a fuzzy regression interval is decided by the collected data and a confiden...
We extend fuzzy linear regression analysis (single equation model) to simultaneous equations. We est...
This paper deals with fuzzy regression analysis in presence of multivariate symmetric fuzzy response...
In performing a fuzzy multiple linear regression model, important topics are: to measure the fitting...
Abstract—A novel approach is introduced to construct a fuzzy regression model when both input data a...
[[abstract]]The method for obtaining the fuzzy estimates of regression parameters with the help of &...
We present an efficient method for selecting important input variables when building a fuzzy model f...
AbstractBy considering two criteria of minimum total sum of vagueness and minimum total sum of squar...
In fuzzy domain, a variable (vague linguistic term) often depends not only on a single variable but ...
In order to estimate fuzzy regression models, possibilistic and least-squares procedures can be cons...
[[abstract]]In this study, using necessity analysis, we located a fuzzy regression interval that is ...
[[abstract]]Fuzzy linear regression was originally introduced by Tanaka et al. To cope with differen...
In the classical multiple regression modeling, there might be some insignificant input variables. Th...
Market researches and opinion polls usually include customers’ responses as verbal labels of sets wi...
Abstract change points. In addition, setting the change points to derive a piecewise fuzzy regressio...
[[abstract]]The range of a fuzzy regression interval is decided by the collected data and a confiden...
We extend fuzzy linear regression analysis (single equation model) to simultaneous equations. We est...
This paper deals with fuzzy regression analysis in presence of multivariate symmetric fuzzy response...
In performing a fuzzy multiple linear regression model, important topics are: to measure the fitting...
Abstract—A novel approach is introduced to construct a fuzzy regression model when both input data a...
[[abstract]]The method for obtaining the fuzzy estimates of regression parameters with the help of &...
We present an efficient method for selecting important input variables when building a fuzzy model f...
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