Improvements of Analog Neural Networks Based on Kalman Filter

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dc.contributor.authorTobes, Z.
dc.contributor.authorRaida, Zbyněk
dc.coverage.issue1cs
dc.coverage.volume11cs
dc.date.accessioned2016-04-28T11:56:49Z
dc.date.available2016-04-28T11:56:49Z
dc.date.issued2002-04cs
dc.description.abstractIn the paper, original improvements of recurrent analog neural networks, which are based on Kalman filter, are presented. These improvements eliminate some disadvantages of the classical Kalman neural network and enable a real time processing of quickly changing signals, which appear in adaptive antennas and similar applications. This goal is reached using such circuit elements, which increase the convergence rate of the network and decrease the dependence of convergence rate on the ratio of eigenvalues of the correlation matrix of input signals.en
dc.formattextcs
dc.format.extent6-13cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2002, vol. 11, č. 1, s. 6-13. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/58135
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2002/02_01_06_13.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectKalman filteren
dc.subjectanalog recurrent neural networksen
dc.subjectcon-vergence rateen
dc.subjectstabilityen
dc.titleImprovements of Analog Neural Networks Based on Kalman Filteren
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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