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۱Applying the Mahalanobis–Taguchi System to Vehicle Ride
اطلاعات انتشار: Journal of Industrial and Systems Engineering، اول،شماره۳، Fall ۲۰۰۷، سال
تعداد صفحات: ۹
The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance 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 Mahalanobis Taguchi System is of interest because of its reported accuracy in forecasting 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 Mahalanobis–Taguchi System and its application to identify a reduced set of useful variables in multidimensional systems.

۲Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the Mahalanobis–Taguchi System
اطلاعات انتشار: Journal of Industrial and Systems Engineering، اول،شماره۴، Winter ۲۰۰۸، سال
تعداد صفحات: ۱۲
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. MTS is of interest because of its reported accuracy in forecasting 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 Adjoint Matrix Approach to MTS for vehicle braking to identify a reduced set of useful variables in multidimensional systems.
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