| 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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