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dc.contributor.authorMu, X.
dc.contributor.authorYu, J.
dc.contributor.authorWang, S.
dc.date.accessioned2014-12-11T12:45:23Z
dc.date.available2014-12-11T12:45:23Z
dc.date.issued2014-09cs
dc.identifier.citationRadioengineering. 2014, vol. 23, č. 3, s. 939-943. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/36518
dc.description.abstractThe memristor was proposed to characterize the flux-charge relation. We propose the generalized flux-charge relation model of memristor with neural network of smooth hinge functions. There is effective identification algorithm for the neural network of smooth hinge functions. The representation capability of this model is theoretically guaranteed. Any functional flux-charge relation of a memristor can be approximated by the model. We also give application examples to show that the given model can approximate the flux-charge relation of existing piecewise linear memristor model, window function memristor model, and a physical memristor device.en
dc.formattextcs
dc.format.extent939-943cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2014/14_03_0939_0943.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectMemristoren
dc.subjectmathematical modelen
dc.subjectsmooth hinge functionen
dc.subjectneural networken
dc.titleModeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functionsen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue3cs
dc.coverage.volume23cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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