Use of Neural Network Model to Predict of Egg Yield

Year 2018, , 147 - 151, 29.08.2018

Çiğdem Takma Yakut Gavrekçi

https://doi.org/10.13002/jafag4309

Abstract

A neural network is a mathematical model of information processing based on the work of the human brain. An artificial neural network (ANN) is composed of a number of simple processing elements connected together in a network. In this study, the egg yield was predicted based on the individually collected hatching period, line, body weight (BW), age at sexual maturity (ASM) and body weight at sexual maturity (BWSM) records of layers using neural network model. A multilayer perceptron (MLP) ANN model trained by back propagation algorithm is developed for feed-forward neural network learning. From the available data set, training and testing sets were extracted. Goodness of fit of the model was determined with the coefficient of determination (R2), root mean square error (RMSE) and Mean Absolute Deviation (MAD) values. The R2 for training and test sets were estimated to be 0.80 and 0.82, respectively. Lower RMSE and MAD values were obtained. The empirical result shows that neural network can be used for the prediction of egg yield.

Keywords

Neural network, coefficient of determination, layer, egg yield, multilayer perception

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