Fast parametric models for EM design using neural networks and space mapping

dc.creatorRayas-Sánchez, José E.
dc.creatorZhang, Qi J.
dc.date2013-05-21T17:57:02Z
dc.date2013-05-21T17:57:02Z
dc.date2008-06
dc.date.accessioned2023-07-21T21:59:30Z
dc.date.available2023-07-21T21:59:30Z
dc.identifierhttp://hdl.handle.net/11117/600
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7756891
dc.description- Brief introduction to ANNs - EM-based statistical analysis - Input Space Mapping - Linear-Input Neural-Output Space Mapping (LINO-SM) - LINO-SM approach to yield estimation - Constrained Broyden-Based Space Mapping - Training the Output Neuromapping - Example - Conclusions
dc.descriptionITESO, A.C.
dc.formatapplication/pdf
dc.languageeng
dc.publisherIEEE MTT-S Int. Microwave Symp. Workshop Notes and Short Courses
dc.relationIEEE MTT-S Int. Microwave Symp. Workshop Notes and Short Courses;2008
dc.rightshttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdf
dc.subjectLinear-Input Neural-Output Space Mapping (LINO-SM)
dc.subjectElectromagnetic Based Design
dc.subjectNeural Networks
dc.subjectSpace Mapping
dc.titleFast parametric models for EM design using neural networks and space mapping
dc.typeinfo:eu-repo/semantics/conferencePaper


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