Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/6314
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZeng, N-
dc.contributor.authorWang, Z-
dc.contributor.authorLi, Y-
dc.contributor.authorDu, M-
dc.contributor.authorLiu, X-
dc.date.accessioned2012-03-19T11:25:27Z-
dc.date.available2012-03-19T11:25:27Z-
dc.date.issued2012-
dc.identifier.citationIEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(2): 321 - 329, Mar-Apr 2012en_US
dc.identifier.issn1545-5963-
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6051426&tag=1en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/6314-
dc.descriptionThis is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEen_US
dc.description.abstractIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.en_US
dc.description.sponsorshipThis work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectLateral flow immunoassayen_US
dc.subjectConstrained optimizationen_US
dc.subjectExtended Kalman filteringen_US
dc.subjectParameter estimationen_US
dc.subjectSwitching particle swarm optimizationen_US
dc.titleA hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay modelsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TCBB.2011.140-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Information and Knowledge Management-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf1.33 MBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.