Título APPROXIMATING THE SHEEP MILK PRODUCTION CURVE THROUGH THE USE OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS
Autores TORRES JIMÉNEZ, MERCEDES, HERVAS MARTINEZ, CESAR
Publicación externa No
Medio COMPUT OPER RES
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Impacto JCR 0.746
Impacto SJR 1.157
Web http://dx.doi.org/10.1016/j.cor.2004.06.025
Fecha de publicacion 01/01/2005
ISI 000228207700011
Scopus Id 2-s2.0-13544274196
DOI 10.1016/j.cor.2004.06.025
Abstract This 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 Extended-Delta-Bar-Delta. © 2004 Elsevier Ltd. All rights reserved.
Palabras clave Functions; Genetic algorithms; Nonlinear systems; Pattern recognition; Problem solving; Regression analysis; Adaptive learning process; Gamma functions; Pruning algorithms; Sheep milk production; Neural networks
Miembros de la Universidad Loyola

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