Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21275
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dc.contributor.authorGuo, J-
dc.contributor.authorWang, Z-
dc.contributor.authorZou, L-
dc.contributor.authorZhao, Z-
dc.date.accessioned2020-07-27T15:04:48Z-
dc.date.available2020-07-25-
dc.date.available2020-07-27T15:04:48Z-
dc.date.issued2020-07-25-
dc.identifier4134-
dc.identifier.citationSensors, 2020, 20 (15)en_US
dc.identifier.issn1424-8220-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/21275-
dc.description.abstractThis paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information transmissions over the shared communication channel. The scheduling behavior of the RAP is characterized by a Markov chain with known transition probabilities. On the other hand, the measurement outputs are quantized by the uniform quantizer before being transmitted via the communication channel. The objective of this paper is to devise a nonlinear filter such that, in the simultaneous presence of RAP and UQEs, the filtering error dynamics is exponentially ultimately bounded in mean square (EUBMS). By resorting to the stochastic analysis technique and the Lyapunov stability theory, sufficient conditions are obtained under which the desired nonlinear filter exists, and then the filter design algorithm is presented. At last, two simulation examples are given to validate the proposed filtering strategy.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectuniform quantizationen_US
dc.subjectrandom access protocolen_US
dc.subjectultimately bounded filteringen_US
dc.subjectdiscrete-time systemsen_US
dc.subjecttime-delaysen_US
dc.titleUltimately Bounded Filtering for Time-Delayed Nonlinear Stochastic Systems with Uniform Quantizations under Random Access Protocolen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3390/s20154134-
dc.relation.isPartOfSensors-
pubs.issue15-
pubs.publication-statusPublished online-
pubs.volume20-
dc.identifier.eissn1424-8220-
Appears in Collections:Dept of Computer Science Research Papers

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