AVALIATON OF GAIN WEIGHT ON ANIMALS THROUGH MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS - Lopes, M. L. M.; Chavarette, F. R.; Cossi, A. M. (Revista Brasileira de Engenharia de Biossistemas, 2017)
The weight of an animal becomes an important variable to be studied, from it, we evaluated the growth and nutritional status of the animal, administer drugs and parasiticides and establish the sales value of the animal market. In this paper we approach the development of a proposal for the prediction gain weight in animals by the method of multiple linear regression and a technique based on artificial intelligence, more specifically artificial neural networks. The goal is to develop both methods and apply them in the analysis of weight gain, for this, we took into consideration data acquired by the animal weights and the value of body condition score obtained in a given period. The analysis was performed using data from 12 animals evaluated in three different periods and considering three types of feed: 1- conventional feed (control), 2 - cottonseed and 3 - cottonseed meal and corn-ground. By means of the results of the analysis of the absolute mean percentage error and the coefficient of determination, it can be observed that both methods demonstrate efficiency in the prediction of mass gain in animals