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| Title: | Identifying useful variables for vehicle braking using the adjoint matrix approach to the Mahalanobis-Taguchi system |
| Author (s): | Cudney, Elizabeth Anne Paryani, Kioumars Ragsdell, Kenneth M. |
| Department/Lab Affiliations: | Design Engineering Center Engineering Management & Systems Engineering |
| Keywords: | Mahalanobis distance (MD) Mahalanobis space (reference group) Mahalanobis-Taguchi system (MTS) adjoint matrix orthogonal array (OA) signal-to-noise ratio (SN) |
| Subject Terms: | Pattern recognition systems. |
| Issue Date: | 2008 |
| Publisher: | Iranian Institute of Industrial Engineering |
| Citation: | Cudney, Elizabeth Anne, Kioumars Paryani, and Kenneth M. Ragsdell. "Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the Mahalanobis-Taguchi System.", Journal of Industrial and Systems Engineering, Vol. 1, No. 4, pp. 281-292, Winter 2008. |
| 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. |
| Type: | Article - Journal text |
| In Title: | Journal of Industrial and Systems Engineering |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Policy Unknown FULL COPYRIGHT INFORMATION: |
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| title | Identifying useful variables for vehicle braking using the adjoint matrix approach to the Mahalanobis-Taguchi system |
| contributor.author | Cudney, Elizabeth Anne |
| contributor.author | Paryani, Kioumars |
| contributor.author | Ragsdell, Kenneth M. |
| contributor.deptlab | Design Engineering Center |
| contributor.deptlab | Engineering Management & Systems Engineering |
| subject | Mahalanobis distance (MD) |
| subject | Mahalanobis space (reference group) |
| subject | Mahalanobis-Taguchi system (MTS) |
| subject | adjoint matrix |
| subject | orthogonal array (OA) |
| subject | signal-to-noise ratio (SN) |
| subject.LCSH | Pattern recognition systems. |
| date.issued | 2008 |
| publisher | Iranian Institute of Industrial Engineering |
| identifier.citation | Cudney, Elizabeth Anne, Kioumars Paryani, and Kenneth M. Ragsdell. "Identifying Useful Variables for Vehicle Braking Using the Adjoint Matrix Approach to the Mahalanobis-Taguchi System.", Journal of Industrial and Systems Engineering, Vol. 1, No. 4, pp. 281-292, Winter 2008. |
| identifier.pub.URI | |
| description.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. |
| type | Article - Journal |
| type.DCMIType | text |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights | Policy Unknown |
| rights.URI | |
| relation.isPartOf | Journal of Industrial and Systems Engineering |
| date.accessioned | 2008-09-19T21:44:19Z |
| date.available | 2008-07-21T17:48:09Z |
| identifier.persist.URI |