Fuzzy regression model is developed to construct the relationship between independent variable and dependent variable in a fuzzy environment. In order to increase the explanatory performance of fuzzy regression model, the least-squares method usually is applied to determine the numeric coefficients based on the concept of distance. In this paper, we consider the fuzzy linear regression model with fuzzy input, fuzzy output and crisp parameters and introduce a new distance based on the geometric centroid and incentre points (GCIP) of triangular fuzzy number, merge least-squares method with the new GCIP distance and propose least-squares GCIP distance method. Finally, an example of employee job performance is given to illustrate the effectiveness and feasibility of the method. Comparisons with existing methods show that total estimation error using the same distance criterion, the explanatory performance of the GCIP method is satisfactory, and the calculation is relatively simple.

中文翻译：

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基于几何质心和中心点的模糊回归模型及其在性能评价中的应用

模糊回归模型被开发用于在模糊环境中构建自变量和因变量之间的关系。为了提高模糊回归模型的解释性能，通常采用最小二乘法来确定基于距离概念的数值系数。在本文中，我们考虑了具有模糊输入、模糊输出和清晰参数的模糊线性回归模型，并引入了基于三角模糊数的几何质心和中心点（GCIP）的新距离，将最小二乘法与新的GCIP合并距离并提出最小二乘GCIP距离方法。最后以员工工作绩效为例说明该方法的有效性和可行性。