Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11957
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dc.contributor.authorHu, J-
dc.contributor.authorWang, Z-
dc.contributor.authorLiu, S-
dc.contributor.authorGao, H-
dc.date.accessioned2016-01-29T09:16:53Z-
dc.date.available2016-02-
dc.date.available2016-01-29T09:16:53Z-
dc.date.issued2016-
dc.identifier.citationAutomatica, 64, pp. 155 - 162, (2016)en_US
dc.identifier.issnC-
dc.identifier.issnC-
dc.identifier.issn0005-1098-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0005109815004720-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11957-
dc.description.abstractIn this paper, the recursive state estimation problem is investigated for an array of discrete timevarying coupled stochastic complex networks with missing measurements. A set of random variables satisfying certain probabilistic distributions is introduced to characterize the phenomenon of the missing measurements, where each sensor can have individual missing probability. The Taylor series expansion is employed to deal with the nonlinearities and the high-order terms of the linearization errors are estimated. The purpose of the addressed state estimation problem is to design a time-varying state estimator such that, in the presence of the missing measurements and the random disturbances, an upper bound of the estimation error covariance can be guaranteed and the explicit expression of the estimator parameters is given. By using the Riccati-like difference equations approach, the estimator parameter is characterized by the solutions to two Riccati-like difference equations. It is shown that the obtained upper bound is minimized by the designed estimator parameters and the proposed state estimation algorithm is of a recursive form suitable for online computation. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation scheme.en_US
dc.description.sponsorshipNational Natural Science Foundation of China under Grants 61329301, 61273156 61333012, 11301118 and 11271103, the Youth Science Foundation of Heilongjiang Province of China under Grant QC2015085, the China Postdoctoral Science Foundation under Grants 2015T80482 and 2014M560376, Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 1402004A, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent155 - 162-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectState estimationen_US
dc.subjectTime-varying complex networksen_US
dc.subjectVariance constraintsen_US
dc.subjectMissing measurementsen_US
dc.subjectRecursive approachen_US
dc.subjectRiccati-like difference equationsen_US
dc.titleA variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurementsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.automatica.2015.11.008-
dc.relation.isPartOfAutomatica-
pubs.notespublisher: Elsevier articletitle: A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements journaltitle: Automatica articlelink: http://dx.doi.org/10.1016/j.automatica.2015.11.008 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved.-
pubs.notespublisher: Elsevier articletitle: A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements journaltitle: Automatica articlelink: http://dx.doi.org/10.1016/j.automatica.2015.11.008 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved.-
pubs.publication-statusAccepted-
pubs.publication-statusAccepted-
pubs.volume64-
Appears in Collections:Dept of Computer Science Research Papers

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