Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12811
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYang, F-
dc.contributor.authorDong, H-
dc.contributor.authorWang, Z-
dc.contributor.authorRen, W-
dc.contributor.authorAlsaadi, FE-
dc.date.accessioned2016-06-16T13:20:03Z-
dc.date.available2016-07-12-
dc.date.available2016-06-16T13:20:03Z-
dc.date.issued2016-
dc.identifier.citationNeurocomputing, 197: pp. 205 - 211, (2016)en_US
dc.identifier.issn0925-2312-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0925231216003635-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12811-
dc.description.abstractIn this paper, the non-fragile state estimation problem is investigated for a class of continuous neural networks with time-delays and nonlinear perturbations. The estimator to be designed is of a simple linear structure without requiring the exact information of the activation functions or the time-delays, and is therefore easy to be implemented. Furthermore, the designed estimator gains are allowed to undergo multiplicative parameter variations within a given range and the non-fragility is guaranteed against possible implementation errors. The main purpose of the addressed problem is to design a non-fragile state estimator for the recurrent delayed neural networks such that the dynamics of the estimation error converges to the equilibrium asymptotically irrespective of the admissible parameter variations with the estimator gains. By employing a combination of the Lyapunov functionals and the matrix analysis techniques, sufficient conditions are established to ensure the existence of the desired estimators and the explicit characterization of such estimators are then given via solving a linear matrix inequality. Finally, a simulation example is used to illustrate the effectiveness of the proposed design method.en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grants 61422301 and 61374127,the Outstanding Youth Science Foundation of Heilongjiang Province under Grant JC2015016, the Technology Foundation for Selected Overseas Chinese Scholar from the Ministry of Personnel of China, and the Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent205 - 211-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectRecurrent neural networksen_US
dc.subjectState estimationen_US
dc.subjectNon-fragilityen_US
dc.subjectTime-delaysen_US
dc.subjectLyapunov functionalen_US
dc.subjectMatrix inequalityen_US
dc.titleA new approach to non-fragile state estimation for continuous neural networks with time-delaysen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2016.02.062-
dc.relation.isPartOfNeurocomputing-
pubs.publication-statusAccepted-
pubs.volume197-
Appears in Collections:Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdfFile is embargoed until 12/07/2017216.69 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.