Authors : Mahmood Chamsaz , Saeid Asadpour , Ali Hossein Perai and Ali Khatibjoo
Abstract: Neural Networks offer an alternative to regression analysis for biological modeling. Very little research has been carried out to model fertility using artificial Neural Networks. The aim of this study is evaluation of a Neural Network model to describe relationship between age of broiler breeder and hatchability. Neural Network model was developed with the Matlab program. The predictive quality of the Neural Network model was tested for an external validation set of 10 weeks randomly chosen from 37 weeks. The goodness of model was determined by Mean Square Error (MSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE) and bias. Forecasting error measurements were based on the difference between the model and the observed values. The squared regression coefficient R2 was 0.9934. The overall calculated statistical values MSE, MAD, MAPE, bias and R2 have shown that Neural Network may be used to provide the accurate fit to hatchability of broiler breeder eggs.
Mahmood Chamsaz , Saeid Asadpour , Ali Hossein Perai and Ali Khatibjoo , 2009. Neural Network Model as a New Powerful Tool to Describe and Predict Hatchability of Broiler Breeder Eggs. Journal of Animal and Veterinary Advances, 8: 1349-1352.