Fuzzy regression models are widely used to investigate the relationship between explanatory and response variables for many decision-making applications in fuzzy environments. To include more fuzzy information in observations, this study uses intuitionistic fuzzy numbers (IFNs) to characterize the explanatory and response variables in formulating intuitionistic fuzzy regression (IFR) models. Different from traditional solution methods, such as the least-squares method, in this study, mathematical programming problems are built up based on the criterion of least absolute deviations to establish IFR models with intuitionistic fuzzy parameters. The proposed approach has the advantages that the model formulation is not limited to the use of symmetric triangular IFNs and the signs of the parameters are determined simultaneously in the model formulation process. The prediction performance of the obtained models is evaluated in terms of similarity and distance measures. Comparison results of the performance measures indicate that the proposed models outperform an existing approach.

中文翻译：

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基于最小绝对偏差的直觉模糊回归模型的数学规划方法

模糊回归模型被广泛用于研究解释变量和响应变量之间的关系，用于模糊环境中的许多决策应用。为了在观察中包含更多模糊信息，本研究使用直觉模糊数（IFN）来表征直觉模糊回归（IFR）模型中的解释变量和响应变量。与最小二乘方法等传统求解方法不同，本研究基于最小绝对偏差准则建立数学规划问题，建立具有直觉模糊参数的IFR模型。所提出的方法具有以下优点：模型制定不限于使用对称三角形IFN，并且在模型制定过程中同时确定参数的符号。根据相似度和距离度量评估获得的模型的预测性能。性能指标的比较结果表明，所提出的模型优于现有方法。