Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models

Abstract

In this study, artificial neural networks and fuzzy inference systems and the combination of these two are employed to develop predictive models to input and output parameters for broiler production. The data was randomly collected from 70 broiler farms in northwestern Iran. The energy used in broiler production was determined to be fuel, feed, and electricity; these were selected as input parameters for the models. The corresponding output energies (broilers and manure) were used as output variables. Linear regression, the multi‐layer perceptron and the radial basis function as the methods of artificial neural networks, and the adaptive neuro‐fuzzy inference system were simulated and compared for prediction of broiler and manure energy. A comparison of the results showed that radial basis function recorded higher coefficient of determination (0.996 and 0.995 for broiler and manure) and lower root mean square error (0.016 and 0.02 for broiler and manure) values to make it the best predictor of outputs. This was followed in order by the adaptive neuro‐fuzzy inference system, linear regression, and multi‐layer perceptron models. © 2016 American Institute of Chemical Engineers Environ Prog, 36: 577–585, 2017