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dc.contributor.authorAkan, Aydın
dc.contributor.authorSaatçı, Esra
dc.date.accessioned2016-05-04T12:07:30Z
dc.date.available2016-05-04T12:07:30Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/11413/1272
dc.description.abstractTime-domain approach to inverse modeling of respiratory system requires estimation of the parameters from the noisy observation. In this work, states and parameters of the linear lung models are estimated simultaneously by dual Kalman filter where the algorithm use two-observation forms. We also employ Kalman smoother for fine tuning the parameters. It is found that the state estimates are more robust to initial filter parameters than the model parameter convergences. Both viscoelastic and the Mead models yielded encouraging results and compatible estimator variances.tr_TR
dc.language.isoen_UStr_TR
dc.publisherSpringer, 233 Spring Street, New York, Ny 10013, United Statestr_TR
dc.relation4Th European Conference Of The International Federation For Medical And Biological Engineeringtr_TR
dc.subjectlinear lung modeltr_TR
dc.subjectviscoelastic modeltr_TR
dc.subjectMead modeltr_TR
dc.subjectdual Kalman filtertr_TR
dc.subjectCOPDtr_TR
dc.subjectrespiratory mechanicstr_TR
dc.subjectDoğrusal akciğer modelitr_TR
dc.subjectviskoelastik modelitr_TR
dc.subjectÇift Kalman filtresitr_TR
dc.subjectsolunum mekaniğitr_TR
dc.subjectKOAHtr_TR
dc.titleDual Kalman Filter based State-Parameter Estimation in Linear Lung Modelstr_TR
dc.typeArticletr_TR
dc.contributor.authorIDTR112197tr_TR
dc.contributor.authorIDTR2918tr_TR
dc.identifier.wos299998500067
dc.identifier.wos299998500067en
dc.identifier.scopus2-s2.0-70350674953
dc.identifier.scopus2-s2.0-70350674953en


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