Neural networks applied to signal processing

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dc.contributor.advisorTummala, Murali
dc.contributor.authorBaehre, Mark D.
dc.contributor.departmentElectrical and Computer Engineering
dc.contributor.secondreaderTherrien, Charles W.
dc.dateSeptember 1989
dc.date.accessioned2013-01-23T21:56:06Z
dc.date.available2013-01-23T21:56:06Z
dc.date.issued1989-09
dc.description.abstractThe relationship between the structure of a neural network and its ability to perform nonlinear mapping is analyzed. A new algorithm, called the conjugate gradient optimization method, for calculating the weights and thresholds of a neural network is presented. The performance of the conjugate gradient algorithm is then compared to the well known backpropagation method and shown to be more computationally efficient. A neural network using the conjugate gradient algorithm is then applied to three simple examples to demonstrate its signal processing capabilities. The first example illustrates the ability of the neural network to perform classification. The second compares the performance of a one-step linear predictor to a neural network for a nonlinear chaotic time series. The neural network predictor is shown to provide much greater accuracy than its linear counterpart. The final application presented demonstrates the ability of a neural network to perform channel equalization for a nonmininmum phase channel. Its performance is then compared to its linear equivalent.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.serviceCaptain, United State Armyen_US
dc.description.urihttp://archive.org/details/neuralnetworkspp1094526101
dc.format.extent96 p.en_US
dc.identifier.urihttps://hdl.handle.net/10945/26101
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.subject.authorNeural networken_US
dc.subject.authorbackpropagationen_US
dc.subject.authorconjugate gradient methoden_US
dc.subject.authorFibonacci line searchen_US
dc.subject.authornonlinear signal processingen_US
dc.subject.authorchannel equalizationen_US
dc.subject.authorartificial intelligenceen_US
dc.subject.authorcomputer architectureen_US
dc.titleNeural networks applied to signal processingen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.levelProfessional Degreeen_US
etd.thesisdegree.nameM.S. in Electrical Engineeringen_US
etd.thesisdegree.nameDegree of Electrical Engineeren_US
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