Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23454
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dc.contributor.authorZhao, D-
dc.contributor.authorWang, Z-
dc.contributor.authorWei, G-
dc.contributor.authorLiu, X-
dc.date.accessioned2021-11-08T13:00:52Z-
dc.date.available2021-11-08T13:00:52Z-
dc.date.issued2020-08-19-
dc.identifier.citationZhao, D., Wang, Z., Wei, G. and Liu, X. (2021) 'Nonfragile H<inf>∞</inf>State Estimation for Recurrent Neural Networks with Time-Varying Delays: On Proportional-Integral Observer Design', IEEE Transactions on Neural Networks and Learning Systems, 2021, 32 (8), pp. 3553 - 3565. doi: 10.1109/TNNLS.2020.3015376.en_US
dc.identifier.issn2162-237X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/23454-
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61933007, 61873148 and 61873169); 10.13039/501100000288-Royal Society of the U.K.; 10.13039/100005156-Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent3553 - 3565-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectH∞ performanceen_US
dc.subjectnonfragile state estimationen_US
dc.subjectproportional–integral observer (PIO)en_US
dc.subjectrandomly occurring gain variations (ROGVs)en_US
dc.subjectrecurrent neural networks (RNNs)en_US
dc.subjecttime-varying delays (TVDs)en_US
dc.titleNonfragile H<inf>∞</inf>State Estimation for Recurrent Neural Networks with Time-Varying Delays: On Proportional-Integral Observer Designen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TNNLS.2020.3015376-
dc.relation.isPartOfIEEE Transactions on Neural Networks and Learning Systems-
pubs.issue8-
pubs.publication-statusPublished-
pubs.volume32-
dc.identifier.eissn2162-2388-
Appears in Collections:Dept of Computer Science Research Papers

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