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Equine Gait Data Analysis using Signal Transforms as a Preprocessor to Back Propagation Networks

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dc.contributor.advisor Calvert, David
dc.contributor.author Cheung, Edwin H.
dc.date.accessioned 2014-05-27T18:17:52Z
dc.date.available 2014-05-27T18:17:52Z
dc.date.copyright 2014-04
dc.date.created 2014-05-22
dc.date.issued 2014-05-27
dc.identifier.uri http://hdl.handle.net/10214/8150
dc.description.abstract This thesis examines using Back Propagation network in the analysis of equine gait data. Back Propagation networks are capable of classifying non-linear data sets, but are not usually built to handle time series data. By using Fourier and wavelet transforms as a pre-processor, the Back Propagation network is then able to overcome this hurdle. It was then able to analyze and classify gait, shoeing and direction in the gait data quite accurately and effectively. Several methods proved to be more effective than others. en_US
dc.language.iso en en_US
dc.rights Attribution 2.5 Canada *
dc.rights.uri http://creativecommons.org/licenses/by/2.5/ca/ *
dc.subject Equine Gait en_US
dc.subject Neural Network en_US
dc.subject Fourier Transform en_US
dc.subject Gait Analysis en_US
dc.subject Signal Transform en_US
dc.subject Wavelet Transform en_US
dc.subject Back Propagation en_US
dc.title Equine Gait Data Analysis using Signal Transforms as a Preprocessor to Back Propagation Networks en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
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Attribution 2.5 Canada Except where otherwise noted, this item's license is described as Attribution 2.5 Canada