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espanolObjetivo. Modelar la curva del crecimiento de aves de la linea Lohmann LSL utilizando modelos no lineales (MNL), no lineales mixtos (MNLM) y redes neuronales artificiales (RNA). Materiales y metodos. Periodicamente se pesaron 33 aves en promedio, desde el dia 21 al 196 de vida para un total de 558 registros individuales de peso. En el ajuste de la curva de crecimiento se utilizaron los modelos: no lineal de Von Bertalanffy (MNL), no lineal Mixto de Von Bertalanffy (MNLM) y redes neuronales artificiales (RNA). Los modelos se compararon con coeficiente de correlacion y medidas de presicion cuadrado medio del error (CME), desviacion media absoluta (MAD) y porcentaje de la media absoluta del error (MAPE). Resultados. Los valores de correlacion entre los datos reales y estimados, fueron 0.999, 0.990 y 0.986 para MNLM, RNA y MNL respectivamente. El modelo mas preciso con base en los criterios MAPE, MAD y CME fue el MNLM, seguido por la RNA. La grafica de prediccion generada la RNA es similar a la del MNLM. La RNA presento un desempeno superior al MLN. Conclusiones. El mejor modelo para la prediccion de curvas de crecimiento de aves comerciales de la linea Lohmman LSL hasta los 196 dias de edad, con multiples mediciones por animal en el tiempo, fue el MNLM. La RNA presento un desempeno superior al MNL. EnglishObjective. Modeling the pullet growth curve of the Lohmann LSL line, by using nonlinear model (MNL), nonlinear mixed model (MNLM) and artificial neural networks (ANN). Materials and methods. An average of 33 birds, were weighed from day 21 to 196 of life for 558 individual weight records. To adjust the growth curve the following models were used: nonlinear Von Bertalanffy (MNL), nonlinear mixed Von Bertalanffy (MNLM) and artificial neural networks (RNA). The models were compared with a correlation coefficient and precision measurements: mean square error (MSE), Mean Absolute Deviation (MAD) and the mean absolute percentage error (MAPE). Results. Correlation values, between actual and estimated data, were 0.999, 0.990 and 0.986 for MNLM, RNA and MNL respectively. The most accurate model based on the MAPE, MAD and CME criteria was MNLM followed by RNA. The prediction graph for RNA was similar to MNLM. The RNA performance was higher than MLN. Conclusions. The best model for the prediction of growth curves of commercial Lohmman LSL birds to 196 days of age, was the MNLM, with multiple measurements per animal at the time. RNA performance was higher MLN.

Modelación del crecimiento de pollitas Lohmann LSL con redes neuronales y modelos de regresión no lineal