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Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms

dc.contributor.authorTorres Jiménez, Mercedes 
dc.contributor.authorHervas Martínez, César
dc.contributor.authorAmador Hidalgo, Francisco
dc.description.abstractThis paper examines the potential of a neural network coupled with genetic algorithms to recognize the parameters that define the production curve of sheep milk, in which production is time-dependent, using solely the data registered in the animals’ first controls. This enables the productive capacity of the animal to be identified more rapidly and leads to a faster selection process in determining the best producers. For this purpose we employ a network with a single hidden layer, using the property of “universal approximation”. To find the number of nodes to be included in this layer, genetic and pruning algorithms are applied. Results thus obtained applying genetic and pruning algorithms are found to be better than other models which exclusively apply the classical learning algorithm
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.titleApproximating the sheep milk production curve through the use of artificial neural networks and genetic algorithmses
dc.journal.titleComputers & Operations Researches
dc.subject.keywordartificial neural networkses
dc.subject.keywordgenetic algorithmses
dc.subject.keywordpruning algorithmses
dc.subject.keywordNon-linear regressiones
dc.subject.keywordgamma functiones

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional