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APPLYING THE MAHALANOBIS-TAGUCHI SYSTEM TO VEHICLE HANDLING

机译:将Mahalanobis-Taguchi系统应用于车辆处理

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The Mahalanobis Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data. Mahalanobis Distance (MD) is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The MTS is of interest because of its reported accuracy in forecasting from small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This paper presents the application of the MTS, its applicability in identifying a reduced set of useful variables in multidimensional systems.
机译:Mahalanobis Taguchi系统(MTS)是多元数据的诊断和预测方法。 Mahalanobis距离(MD)是基于可以识别和分析的变量和不同模式之间的相关性的度量,这些模式是关于基础或参考组的识别和分析。由于其报告从小相关数据集预测的准确性,MTS非常感兴趣。这是消费者车辆评级遇到的数据类型。 MTS能够减少维度和基于MD值开发比例的能力。 MTS从具有等效相关性的完整数据集中识别一组有用的变量,并且相当较少的时间和数据。本文介绍了MTS的应用,其在识别多维系统中识别一组有用变量的适用性。

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