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dc.contributor.advisorTummala, Murali
dc.contributor.authorWellington, Charles H.
dc.date.accessioned2013-01-23T22:07:49Z
dc.date.available2013-01-23T22:07:49Z
dc.date.issued1991-12
dc.identifier.urihttp://hdl.handle.net/10945/26899
dc.description.abstractThis thesis investigates the application of backpropagation neural networks as an alternative to adaptive filtering at the NUWES test ranges. To facilitate the investigation, a model of the test range is developed. This model accounts for acoustic transmission losses, the effects of doppler shift, multipath, and finite propagation time delay. After describing the model, the backpropagation neural network algorithm and feature selection for the network are explained. Then, two schemes based on the network's output, signal waveform recovery and binary code recovery, are applied to the model. Simulation results of the signal waveform recovery and direct code recovery schemes are presented for several scenarios.en_US
dc.description.urihttp://archive.org/details/backpropagationn1094526899
dc.format.extent68 p.;28 cm.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.titleBackpropagation neural network for noise cancellation applied to the NUWES test rangesen_US
dc.typeThesisen_US
dc.contributor.secondreaderTitus, Harold A.
dc.contributor.corporateNaval Postgraduate School
dc.contributor.schoolNaval Postgraduate School
dc.contributor.departmentElectrical Engineering
dc.subject.authorBackpropagationen_US
dc.subject.authorneural networksen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameM.S. in Electrical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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