模糊线性回归模型的约束最小二乘估计(英文)

A Restricted Least Squares Estimation for Fuzzy Linear Regression Models

自Tanaka等1982年提出模糊回归概念以来,该问题已得到广泛的研究。作为主要估计方法之一的模糊最小二乘估计以其与统计最小二乘估计的密切联系更受到人们的重视。本文依据适当定义的两个模糊数之间的距离,提出了模糊线性回归模型的一个约束最小二乘估计方法,该方法不仅能使估计的模糊参数的宽度具有非负性而且估计的模糊参数的中心线与传统的最小二乘估计相一致。最后,通过数值例子说明了所提方法的具体应用。

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extend, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. This method can obtain not only non-negative spreads of the estimated fuzzy parameters and a traditional least squares center line of the fitted fuzzy output which is of particular! importance to a decision maker. Numerical examples are further considered to demonstrate the practical application of the proposed method.