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dc.contributor.authorYulong Huangen_US
dc.contributor.authorYonggang Zhangen_US
dc.contributor.authorNing Lien_US
dc.date.accessioned2020-05-20T08:58:20Z-
dc.date.available2020-05-20T08:58:20Z-
dc.date.issued2016en_US
dc.identifier.issn1751-8644en_US
dc.identifier.issn1751-8652en_US
dc.identifier.other10.1049/iet-cta.2015.1092en_US
dc.identifier.urihttp://localhost/handle/Hannan/147822en_US
dc.identifier.urihttp://localhost/handle/Hannan/602743-
dc.description.abstractIn this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model.en_US
dc.publisherIEEEen_US
dc.relation.haspart7449097.pdfen_US
dc.subjectunknown constant latency probability estimation algorithm|Newton approach|M-step|expectation maximisation approach|maximisation step|univariate nonstationary growth model|maximum likelihood criterion|complete data log-likelihood function|ML criterion|one-step randomly delayed measurements|E-step|nonlinear systems|expectation stepen_US
dc.titleLatency probability estimation of non-linear systems with one-step randomly delayed measurementsen_US
dc.typeArticleen_US
dc.journal.volume10en_US
dc.journal.issue7en_US
dc.journal.titleIET Control Theory & Applicationsen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorYulong Huangen_US
dc.contributor.authorYonggang Zhangen_US
dc.contributor.authorNing Lien_US
dc.date.accessioned2020-05-20T08:58:20Z-
dc.date.available2020-05-20T08:58:20Z-
dc.date.issued2016en_US
dc.identifier.issn1751-8644en_US
dc.identifier.issn1751-8652en_US
dc.identifier.other10.1049/iet-cta.2015.1092en_US
dc.identifier.urihttp://localhost/handle/Hannan/147822en_US
dc.identifier.urihttp://localhost/handle/Hannan/602743-
dc.description.abstractIn this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model.en_US
dc.publisherIEEEen_US
dc.relation.haspart7449097.pdfen_US
dc.subjectunknown constant latency probability estimation algorithm|Newton approach|M-step|expectation maximisation approach|maximisation step|univariate nonstationary growth model|maximum likelihood criterion|complete data log-likelihood function|ML criterion|one-step randomly delayed measurements|E-step|nonlinear systems|expectation stepen_US
dc.titleLatency probability estimation of non-linear systems with one-step randomly delayed measurementsen_US
dc.typeArticleen_US
dc.journal.volume10en_US
dc.journal.issue7en_US
dc.journal.titleIET Control Theory & Applicationsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7449097.pdf901.3 kBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYulong Huangen_US
dc.contributor.authorYonggang Zhangen_US
dc.contributor.authorNing Lien_US
dc.date.accessioned2020-05-20T08:58:20Z-
dc.date.available2020-05-20T08:58:20Z-
dc.date.issued2016en_US
dc.identifier.issn1751-8644en_US
dc.identifier.issn1751-8652en_US
dc.identifier.other10.1049/iet-cta.2015.1092en_US
dc.identifier.urihttp://localhost/handle/Hannan/147822en_US
dc.identifier.urihttp://localhost/handle/Hannan/602743-
dc.description.abstractIn this study, the authors focus on estimating the unknown constant latency probability of non-linear systems with one-step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E-step) and the maximisation step (M-step). In the E-step, the expectation of the complete data log-likelihood function is approximately computed based on the currently estimated latency probability, and in the M-step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non-stationary growth model.en_US
dc.publisherIEEEen_US
dc.relation.haspart7449097.pdfen_US
dc.subjectunknown constant latency probability estimation algorithm|Newton approach|M-step|expectation maximisation approach|maximisation step|univariate nonstationary growth model|maximum likelihood criterion|complete data log-likelihood function|ML criterion|one-step randomly delayed measurements|E-step|nonlinear systems|expectation stepen_US
dc.titleLatency probability estimation of non-linear systems with one-step randomly delayed measurementsen_US
dc.typeArticleen_US
dc.journal.volume10en_US
dc.journal.issue7en_US
dc.journal.titleIET Control Theory & Applicationsen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7449097.pdf901.3 kBAdobe PDFThumbnail
Preview File