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dc.creatorPathak, Jogen K
dc.date.accessioned2012-06-07T22:24:51Z
dc.date.available2012-06-07T22:24:51Z
dc.date.created1991
dc.date.issued1991
dc.identifier.urihttps://hdl.handle.net/1969.1/ETD-TAMU-1991-THESIS-P295
dc.descriptionDue to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item.en
dc.descriptionIncludes bibliographical references.en
dc.description.abstractNot availableen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherTexas A&M University
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.subjectcomputer science.en
dc.subjectMajor computer science.en
dc.subject.lcshNeural networks (Computer science)en
dc.subject.lcshPerceptrons.en
dc.subject.lcshNumerical analysis - Acceleration of convergence.en
dc.titleA new acceleration technique for the backpropagation neural network paradigmen
dc.typeThesisen
thesis.degree.disciplinecomputer scienceen
thesis.degree.nameM.S.en
thesis.degree.levelMastersen
dc.type.genrethesisen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen


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