Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21747
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dc.contributor.authorShen, B-
dc.contributor.authorWang, Z-
dc.contributor.authorQiao, H-
dc.date.accessioned2020-10-31T17:03:51Z-
dc.date.available2016-02-19-
dc.date.available2020-10-31T17:03:51Z-
dc.date.issued2016-02-19-
dc.identifier.citationShen, B., Wang, Z., & Qiao, H. (2017). Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. IEEE Transaction on Neural Networks and Learning Systems, 28(5), 1152-1163. doi:10.1109/tnnls.2016.2516030en_US
dc.identifier.issn2162-237X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/21747-
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100003395-Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning; Fundamental Research Funds for the Central Universities; DHU Distinguished Young Professor Program.en_US
dc.format.extent1152 - 1163-
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectevent-triggered state estimationen_US
dc.subjectexponentially ultimate boundednessen_US
dc.subjectincomplete measurementsen_US
dc.subjectneural networksen_US
dc.subjectquantizationsen_US
dc.subjectsensor saturationsen_US
dc.subjectstochastic parametersen_US
dc.titleEvent-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks with Stochastic Parameters and Incomplete Measurementsen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TNNLS.2016.2516030-
dc.relation.isPartOfIEEE Transactions on Neural Networks and Learning Systems-
pubs.issue5-
pubs.publication-statusPublished-
pubs.volume28-
dc.identifier.eissn2162-237X-
Appears in Collections:Dept of Computer Science Research Papers

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