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dc.contributor.advisorCollins, Daniel Joseph
dc.contributor.authorPinkepank, James Alan
dc.dateSeptember 1994
dc.date.accessioned2014-08-13T20:27:35Z
dc.date.available2014-08-13T20:27:35Z
dc.date.issued1994-09
dc.identifier.urihttp://hdl.handle.net/10945/43010
dc.descriptionApproved for public release, distribution unlimiteden_US
dc.description.abstractIonospheric models have been developed to interpret Relocatable Over-the-Horizon Radar data. This thesis examines the applicability of neural networks to ionospheric modeling in support of Relocatable Over-the-Horizon Radar. Two neural networks were used for this investigation. The flrst network was trained and tested on experimental ionospheric sounding data. Results showed neural networks are excellent at modeling ionospheric data for a given day. The second network was trained on ionospheric models and tested on experimental data. Results showed neural networks are able to learn many ionospheric models and the modeling network generally agreed with the experimental data.en_US
dc.format.extent41 p.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleThe applicability of neural networks to ionospheric modeling in support of relocatable over-the-horizon radaren_US
dc.typeThesisen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.subject.authorNAen_US
dc.description.serviceU.S. Navy (USN) authoren_US
dc.identifier.oclcocn640620861
etd.thesisdegree.nameM.S. in Aeronautical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineAeronautical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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