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dc.contributor.authorRaida, Zbyněk
dc.date.accessioned2016-05-02T12:06:22Z
dc.date.available2016-05-02T12:06:22Z
dc.date.issued2001-12cs
dc.identifier.citationRadioengineering. 2001, vol. 10, č. 4, s. 24-35. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/58207
dc.description.abstractNeural networks are electronic systems which can be trained to remember behavior of a modeled structure in given operational points, and which can be used to approximate behavior of the structure out of the training points. These approximation abilities of neural nets are demonstrated on modeling a frequency-selective surface, a microstrip transmission line and a microstrip dipole. Attention is turned to the accuracy and to the efficiency of neural models. The association of neural models and genetic algorithms, which can provide a global design tool, is discussed.en
dc.formattextcs
dc.format.extent24-35cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2001/01_04_24_35.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectNeural networksen
dc.subjectgenetic algorithmsen
dc.subjectplanar transmis-sion linesen
dc.subjectfrequency selective surfacesen
dc.subjectmicrostrip antennasen
dc.subjectmodelingen
dc.subjectoptimizationen
dc.titleNeural Networks in Antennas and Microwaves: A Practical Approachen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue4cs
dc.coverage.volume10cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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