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3D FcRM modelling in miles per gallon of cars

机译:每加仑汽车的英里数进行3D FcRM建模

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摘要

The new fuzzy c-regression modeling (FcRM) are widely used in order to fit switching regression models. Minimization of objective function yields immediate estimates for different c regression models. The functions of model, estimation technique and results are discussed in this paper. A case study in miles per gallon (MPG) of different cars using the FcRM modeling was carried out. The 3D graph for significant independent variables for FcRM clustering is shown in this study. The comparison between multiple linear regression and FcRM modeling were done. The mean square error (MSE) was used to find the better model. It was found that the FcRM modeling with lower MSE to be the better model and has great capability in predicting the dependent variable effectively.
机译:为了适应切换回归模型,新的模糊c回归建模(FcRM)被广泛使用。目标函数的最小化可立即得出不同c回归模型的估计值。本文讨论了模型的功能,估计技术和结果。使用FcRM模型对不同汽车的英里/加仑(MPG)进行了案例研究。这项研究显示了FcRM聚类的重要独立变量的3D图。进行了多元线性回归和FcRM建模之间的比较。均方误差(MSE)用于找到更好的模型。发现具有较低MSE的FcRM建模是更好的模型,并且具有有效地预测因变量的能力。

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