TY - JOUR ID - 3935 TI - Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the Mahalanobis-Taguchi System JO - Journal of Industrial and Systems Engineering JA - JISE LA - en SN - 1735-8272 AU - Cudney, Elizabeth A. AU - Paryani, Kioumars AU - Ragsdell, Kenneth M. AD - University of Missouri – Rolla, Rolla, Missouri 65409 USA AD - Lawrence Technological University, Southfield, Massachusetts 02139 USA Y1 - 2008 PY - 2008 VL - 1 IS - 4 SP - 281 EP - 292 KW - Mahalanobis-Taguchi system (MTS) KW - Mahalanobis distance (MD) KW - Adjoint matrix KW - Pattern Recognition KW - Orthogonal array (OA) KW - Signal-to-noise ratio (SN) KW - Mahalanobis space (reference group) DO - N2 - 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. UR - https://www.jise.ir/article_3935.html L1 - https://www.jise.ir/article_3935_ec52a34b9c898b56a2b31d65ca64675f.pdf ER -