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dc.creatorHamm, Lonnie
dc.creatorBrorsen, B. Wade
dc.date2002-10-01T14:42:53Z
dc.date2002-10-01T14:42:53Z
dc.date2002
dc.date.accessioned2012-06-14T22:37:42Z
dc.date.available2012-06-14T22:37:42Z
dc.date.issued2012-06-14
dc.identifier5721
dc.identifierhttp://purl.umn.edu/36631
dc.identifier.urihttps://repositorio.leon.uia.mx/xmlui/123456789/53034
dc.descriptionTraining a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm. This study uses Monte-Carlo simulations to determine the relative efficiency of a local search algorithm to 9 stochastic global algorithms. The computational requirements of the global algorithms are several times higher than the local algorithm and there is little gain in using the global algorithms to train neural networks.
dc.format23
dc.formatapplication/pdf
dc.languageEnglish
dc.languageen
dc.publisherAgEcon Search
dc.relationWestern Agricultural Economics Association>2002 Annual Meeting, July 28-31, 2002, Long Beach, California
dc.relationSelected Paper of the 2002 Annual Meeting, July 28-31, 2002, Long Beach, California
dc.subjectResearch Methods/ Statistical Methods
dc.titleGLOBAL OPTIMIZATION METHODS
dc.typeConference Paper or Presentation


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