The application of classification methods to the gross error detection problem
All process measurements are corrupted by the presence of measurement error to some degree.The attenuation of the measurement error, especially large gross errors, can increase the value of the process measurements. Gross error detection has typically been performed through rigorous statistical hypo...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
International Federation of Automatic Control
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/16847 |
| _version_ | 1848749294377500672 |
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| author | Gerber, E. Auret, L. Aldrich, Chris |
| author2 | Boje |
| author_facet | Boje Gerber, E. Auret, L. Aldrich, Chris |
| author_sort | Gerber, E. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | All process measurements are corrupted by the presence of measurement error to some degree.The attenuation of the measurement error, especially large gross errors, can increase the value of the process measurements. Gross error detection has typically been performed through rigorous statistical hypothesis testing. The assumptions required to derive the necessary statistical properties are restrictive, which lead to investigation of alternative approaches, such as artificial neural networks. This paper reports the results of an investigation into the utility of classification trees and linear and quadratic classification functions for resolving the gross error detection and identification problems. |
| first_indexed | 2025-11-14T07:18:39Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16847 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:18:39Z |
| publishDate | 2014 |
| publisher | International Federation of Automatic Control |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-168472023-02-13T08:01:37Z The application of classification methods to the gross error detection problem Gerber, E. Auret, L. Aldrich, Chris Boje Edward Xia Xiaohua classification trees identification gross error detection classification functions All process measurements are corrupted by the presence of measurement error to some degree.The attenuation of the measurement error, especially large gross errors, can increase the value of the process measurements. Gross error detection has typically been performed through rigorous statistical hypothesis testing. The assumptions required to derive the necessary statistical properties are restrictive, which lead to investigation of alternative approaches, such as artificial neural networks. This paper reports the results of an investigation into the utility of classification trees and linear and quadratic classification functions for resolving the gross error detection and identification problems. 2014 Conference Paper http://hdl.handle.net/20.500.11937/16847 International Federation of Automatic Control restricted |
| spellingShingle | classification trees identification gross error detection classification functions Gerber, E. Auret, L. Aldrich, Chris The application of classification methods to the gross error detection problem |
| title | The application of classification methods to the gross error detection problem |
| title_full | The application of classification methods to the gross error detection problem |
| title_fullStr | The application of classification methods to the gross error detection problem |
| title_full_unstemmed | The application of classification methods to the gross error detection problem |
| title_short | The application of classification methods to the gross error detection problem |
| title_sort | application of classification methods to the gross error detection problem |
| topic | classification trees identification gross error detection classification functions |
| url | http://hdl.handle.net/20.500.11937/16847 |