The design of neural networks for the performance estimation of satellite transponders

dc.contributor.authorMussie, Mehari Stefanosen
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T21:29:43Zen
dc.date.adate2010-02-16en
dc.date.available2014-03-14T21:29:43Zen
dc.date.issued1991en
dc.date.rdate2010-02-16en
dc.date.sdate2010-02-16en
dc.description.abstractIt has become increasingly important to improve upon the performance of satellite transponders, whose function is to receive and transmit signals automatically when triggered by an interrogator. The transponder of INTELSAT V whose performance parameters include: noise figure, group delay, gain, frequency translation, and power output is considered for investigation. The focus of this project is to design an Artificial Neural Network (ANN) as a new analytical model and computational technique to assess the intricate interactions between the transponder performance parameters and the environment in order to improve future satellite transponder performance and design. The rationale for the use of ANN as a means of estimating the performance of a transponder lies in their parallel computation, learning ability, optimization capability, distributed data presentation, and ability to handle various tasks that are difficult for traditional computer techniques. Computer analysis tools are used to generate an optimal ANN model that meets the design specifications. Finally, several candidate ANN models are investigated and the proposed models are selected based upon the result that minimizes the mean square error. The analysis will address the design of ANN using hypothetical training and validation data which incorporates a comparative assessment of the ANN estimation accuracy relative to the number of training patterns, iterations, hidden units, and learning parameters. Consequently, the impact of dynamic threshold values to interpret the ANN's response relative to transponder performance specification by the postprocessor is discussed.en
dc.description.degreeMaster of Scienceen
dc.format.extentxi, 98 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-02162010-020056en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-02162010-020056/en
dc.identifier.urihttp://hdl.handle.net/10919/41146en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V851_1991.M988.pdfen
dc.relation.isformatofOCLC# 23843997en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V851 1991.M988en
dc.subject.lcshNeural circuitryen
dc.subject.lcshSatellitesen
dc.subject.lcshTranspondersen
dc.titleThe design of neural networks for the performance estimation of satellite transpondersen
dc.typeMaster's projecten
dc.type.dcmitypeTexten
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen
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