Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the Mahalanobis-Taguchi System

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.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Mahalanobis Distance (MD); Mahalanobis Space (Reference Group); Mahalanobis-Taguchi System (MTS); Adjoint Matrix; Orthogonal Array (OA); Signal-To-Noise Ratio (SN); Pattern recognition systems

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 Iranian Institute of Industrial Engineering, All rights reserved.

Publication Date

01 Jan 2008

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