The application of classification methods to the gross error detection problem
dc.contributor.author | Gerber, E. | |
dc.contributor.author | Auret, L. | |
dc.contributor.author | Aldrich, Chris | |
dc.contributor.editor | Boje | |
dc.contributor.editor | Edward | |
dc.contributor.editor | Xia | |
dc.contributor.editor | Xiaohua | |
dc.date.accessioned | 2017-01-30T11:58:12Z | |
dc.date.available | 2017-01-30T11:58:12Z | |
dc.date.created | 2015-05-22T08:32:25Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Gerber, E. and Auret, L. and Aldrich, C. 2014. The application of classification methods to the gross error detection problem, in 19th World Congress of the International Federation of Automatic Control (IFAC 2014), Aug 24 2014, pp. 11464-11469. Cape Town, South Africa: IFAC. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/16847 | |
dc.description.abstract |
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. | |
dc.publisher | International Federation of Automatic Control | |
dc.subject | classification trees | |
dc.subject | identification | |
dc.subject | gross error detection | |
dc.subject | classification functions | |
dc.title | The application of classification methods to the gross error detection problem | |
dc.type | Conference Paper | |
dcterms.source.startPage | 11464 | |
dcterms.source.endPage | 11469 | |
dcterms.source.title | Proceedings of the 19th IFAC World Congress, 2014, World Congress, Volume# 19 | Part# 1 | |
dcterms.source.series | Proceedings of the 19th IFAC World Congress, 2014, World Congress, Volume# 19 | Part# 1 | |
dcterms.source.isbn | 978-3-902823-62-5 | |
dcterms.source.conference | 19th World Congress of the International Federation of Automatic Control (IFAC 2014) | |
dcterms.source.conference-start-date | Aug 24 2014 | |
dcterms.source.conferencelocation | Cape Town International Convention Centre, Cape Town, South Africa | |
dcterms.source.place | IFAC-PapersOnLine., 10344 Virginia Lee Dr., Centerville, OH 45458, USA | |
curtin.department | Western Australian School of Mines | |
curtin.accessStatus | Fulltext not available |