Abstract
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. Copyright © 2007 SAE International.
Recommended Citation
E. A. Cudney et al., "Identifying Useful Variables for Vehicle Braking using the Adjoint Matrix Approach to the Mahalanobis-Taguchi System," SAE Technical Papers, SAE International, Jan 2007.
The definitive version is available at https://doi.org/10.4271/2007-01-0554
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
2688-3627; 0148-7191
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 SAE International, All rights reserved.
Publication Date
01 Jan 2007
