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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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摘要

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)是多变量数据的诊断和预测方法。马氏距离(MD)是一种基于变量与不同模式之间相关性的度量,可以针对基本组或参考组进行识别和分析。 MTS之所以引起人们的兴趣,是因为据报道其在从小型相关数据集中进行预测的准确性。这是消费类汽车额定值遇到的数据类型。 MTS可以减少尺寸,并可以根据MD值开发刻度。 MTS从完整的数据集中识别出一组有用的变量,它们具有同等的相关性,并且时间和数据要少得多。本文介绍了MTS的应用及其在识别多维系统中一组有用变量的减少中的适用性。

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