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dc.contributor.authorBoonsri Kaewkham-Aien_US
dc.contributor.authorRobert F. Harrisonen_US
dc.date.accessioned2018-09-10T03:16:18Z-
dc.date.available2018-09-10T03:16:18Z-
dc.date.issued2009-12-01en_US
dc.identifier.other2-s2.0-77954180429en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954180429&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/59494-
dc.description.abstractThis paper presents a short-term prediction of the disturbance storm time (DST) index using unscented Kalman filter. Joint and dual estimation methods are studied to examine an improvement of DST index prediction by estimating modal parameters and updating recursively. Comparison between these teachniquies and a fixed model parameter prediction are made in terms of root mean square error (rmse). It is found that joint and dual estimation methods give less rmse than state estimation alone for all DST range, whereas state estimation alone shows better performance than joint and dual estimation for DST below-80 nT.en_US
dc.subjectComputer Scienceen_US
dc.titleD<inf>ST</inf> index prediction using joint and dual unscented Kalman filteren_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings of the 13th IASTED International Conference on Software Engineering and Applications, SEA 2009en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Sheffielden_US
Appears in Collections:CMUL: Journal Articles

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