Comparison of Artificial Neural Network and Multiple Linear Regression for Prediction of Live Weight in Hair Goats

Abstract

Artificial neural networks are artificial intelligence based methods which learns like humans, as humans did from instances. In recent years, artificial neural networks are often preferred in prediction studies of farm animals as like in many different fields as an alternative to regression analyses. In this study, based on measurements of morphologic traits of 475 Hair goats, the impact of different morphological measures on live weight has been modelled by artificial neural networks and multiple linear regression analyses. Comparison of these two models has been done. In the analyses done with the artificial neural networks method three different back propagation algorithms, such as Levenberg-Marquart, Bayesian regularization and Scaled conjugate, have been used. Methods performances have been determined with different criteria as coefficient of determination, mean absolute deviation, root mean square error and mean absolute percentage error. According to the analyses results, it’s noted that artificial neural networks method is more successful than multiple linear regression in prediction of body weight in hair goats. © 2017, Centenary University. All rights reserved.