Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22644
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dc.contributor.authorLuo, Y-
dc.contributor.authorWang, Z-
dc.contributor.authorChen, Y-
dc.contributor.authorYi, X-
dc.date.accessioned2021-05-10T09:03:30Z-
dc.date.available2021-01-01-
dc.date.available2021-05-10T09:03:30Z-
dc.date.issued2021-02-09-
dc.identifier.citationY. Luo, Z. Wang, Y. Chen and X. Yi, "H-infinity State Estimation for Coupled Stochastic Complex Networks with Periodical Communication Protocol and Intermittent Nonlinearity Switching," in IEEE Transactions on Network Science and Engineering,en_US
dc.identifier.issn2327-4697-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/22644-
dc.description.abstractIn this paper, an H-infinity estimation approach is given for an array of coupled stochastic complex networks with intermittent nonlinearity switching. A set of binary random variables are adopted to characterize the intermittent switching behavior of the involved nonlinearities. To effectively alleviate data collisions and save energy, the Round-Robin protocol is utilized to curb network congestions in data communication. For the coupled stochastic complex networks, we design a protocol-based H-infinity estimator that not only resists stochastic disturbances, but also ensures the exponential mean square stability of the desired error system under a given disturbance attenuation level. With the help of the Lyapunov stability and convex optimization theories, sufficient conditions are provided for the expected estimator. Simulations are provided to illustrate the reasonability of our H-infinity approach.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectStochastic complex networksen_US
dc.subjectH performanceen_US
dc.subjectIntermittent nonlinearity switchingen_US
dc.subjectRound-Robin protocolen_US
dc.titleH-infinity State Estimation for Coupled Stochastic Complex Networks with Periodical Communication Protocol and Intermittent Nonlinearity Switchingen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TNSE.2021.3058220-
dc.relation.isPartOfIEEE Transactions on Network Science and Engineering-
pubs.publication-statusPublished-
dc.identifier.eissn2327-4697-
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