This study presents a new approach based on Tanaka's fuzzy linear regression (FLP) algorithm to solve well-known power system economic load dispatch problem (ELD). Tanaka's fuzzy linear regression (FLP) formulation will be employed to compute the optimal solution of optimization problem after linearization. The unknowns are expressed as fuzzy numbers with a triangular membership function that h...

Fuzzy regression models has been traditionally considered as a problem of linear programming. The use of quadratic programming allows to overcome the limitations of linear programming as well as to obtain highly adaptable regression approaches. However, we verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this prob...

this study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. a least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. the proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by...

This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s input and output are fuzzy numbers, and the regression coefficients are clear numbers. This paper considers the parameter estimation and impact analysis based on data deletion. Throug...

Fuzzy linear regression is an interesting tool for handling uncertain data samples as an alternative to a probabilistic approach. This paper sets forth uses a linear regression model for fuzzy variables; the model is optimized through convex methods. A fuzzy linear programming model has been designed to solve the problem with nonlinear fuzzy data by combining the fuzzy arithmetic theory with co...

This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by...

Previous studies on fuzzy linear regression analysis have a common characteristic of increasing spreads for the estimated fuzzy responses as the independent variable increases its magnitude, which is not suitable for general cases. This paper proposes a two-stage approach to construct the fuzzy linear regression model. In the 2rst stage, the fuzzy observations are defuzzi2ed so that the traditi...

Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD), which is a widely used customer-driven approach. In our opinion, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements (CRs) and engineering characteristics (ECs) as well a...

Where fuzzy regression can be applied and, in which conditions fuzzy regression method more appropriate tool for the investigations are identified in this paper. The contrast between fuzzy regression and ordinary regression analysis and three approach of fuzzy regression are summarized. In this paper, we estimated the values of the parameters in the factorial experiment in the textile industry ...

In general fuzzy linear regression, the coefficients of the fuzzy regression model are symmetric triangular fuzzy numbers, while we try to replace them by more general ones, which are asymmetric trapezoidal fuzzy numbers. Possibility of equality between two asymmetric trapezoidal fuzzy numbers is explained by possibility distribution. Two different models are presented in this paper. Furthermor...