Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) pro- posed a modification of fuzzy line... more Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) pro- posed a modification of fuzzy linear regression analysis. Their modification is based on a criterion of minimizing the dierence of the fuzzy membership values between the observed and estimated fuzzy numbers. We show that their method often does not find acceptable fuzzy linear regression coecients and to overcome this shortcoming, propose a modification. Finally, we present two numerical examples to illustrate eciency of the modified method.
This study attempts to develop a regression model when both input data and output data are quasi ... more This study attempts to develop a regression model when both input data and output data are quasi type-2 fuzzy numbers. To estimate the crisp parameters of the regression model, a linear programming model is proposed based on goal programming. To handle the outlier problem, an omission approach is proposed. This approach examines the behavior of value changes in the objective function of proposed model when observations are omitted. In order to illustrate the proposed model, some numerical examples are presented. The applicability of the proposed method is tested on a real data set on soil science. The predictive performance of the model is examined by cross-validation.
Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered... more Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered in outputs and/or in inputs. This paper focuses on both fuzzy inputs and fuzzy outputs. First, some approximations for multiplication of two triangular fuzzy numbers are introduced. Then, to evaluate the fuzzy linear regression, the best approximation is selected to minimize a suitable function via
Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) pro- posed a modification of fuzzy line... more Kim and Bishu (Fuzzy Sets and Systems 100 (1998) 343-352) pro- posed a modification of fuzzy linear regression analysis. Their modification is based on a criterion of minimizing the dierence of the fuzzy membership values between the observed and estimated fuzzy numbers. We show that their method often does not find acceptable fuzzy linear regression coecients and to overcome this shortcoming, propose a modification. Finally, we present two numerical examples to illustrate eciency of the modified method.
This study attempts to develop a regression model when both input data and output data are quasi ... more This study attempts to develop a regression model when both input data and output data are quasi type-2 fuzzy numbers. To estimate the crisp parameters of the regression model, a linear programming model is proposed based on goal programming. To handle the outlier problem, an omission approach is proposed. This approach examines the behavior of value changes in the objective function of proposed model when observations are omitted. In order to illustrate the proposed model, some numerical examples are presented. The applicability of the proposed method is tested on a real data set on soil science. The predictive performance of the model is examined by cross-validation.
Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered... more Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered in outputs and/or in inputs. This paper focuses on both fuzzy inputs and fuzzy outputs. First, some approximations for multiplication of two triangular fuzzy numbers are introduced. Then, to evaluate the fuzzy linear regression, the best approximation is selected to minimize a suitable function via
Uploads
Papers by H Hassanpour