comparison of regression and artificial neural network models in predicting the production performance of laying hens

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear relationship. artificial neural network (ann) technique is used to solve a wide range of problems in science and engineering, particularly for some areas where the mathematical modeling methods fail. nowadays, the anns are one of the most powerful modeling techniques to model complex nonlinear, multidimensional function relationships without any prior assumptions about the nature of the relationships. artificial neural network models are different from mathematical modeling approaches in their ability to learn relationships between dependent and independent variables through the data itself rather than assuming the functional form of the relationships. a well trained ann can be used as a predictive model for a specific application. the prediction by a well-trained ann is normally faster than the mathematical models. several authors have shown greater performances of ann as compared to regression models. an ann model can predict multiple dependent variables based on multiple independent variables, where a mathematical model is only able to predict one dependent variable at a time. therefore, this study was designed to evaluate the prediction of production performance of laying hens using the neural networks and nonlinear regressions. materials and methods: review the four consecutive, information were obtained from a laying hen farm. data mining methods include: three-layer perceptron neural network, four-layer perceptron neural network, radial basis function (rbf) neural network and multiply linear and nonlinear regression. in linear model, the variables of age flock, month of production, feed intake have been considered as the predictor variable and production (percent and egg mass production and feed conversion ratio) have been considered as the response variable. three steps were taken to select an optimal ann model. the first step was to determine the best number of hidden layers, number of neurons in each hidden layer, and activation function. the best models were selected on the basis of training and prediction accuracy. the second step was to work with the selected models to find the optimum epoch size. the third step was to find the optimum learning rate and momentum values. the evaluating method for selecting the optimal ann was based on the minimization of deviations between predicted and measured values. results and discussion: the aim of this study is to obtain an ann model with minimum errors in training and testing. nonlinear regression models were compared with neural network models. all the models are compared using the coefficient of determination (r2) and mean absolute error (mae). the results showed that the artificial neural networks compared the regression models and between different artificial neural networks the rbf model had better curve fitting for laying hen production performance indicators included; egg production (% / b/ d), egg mass (g/ b/ d) and feed conversion ratio in front of age and this fact shows that even for spiral data artificial neural network works well. therefore, we can use these models for complex situations. conclusion: the obtained results revealed that the ann model may efficiently be fitted into the laying hen production performance include percentage and egg mass and feed conversion ratio of hen flocks. results showed that the method of radial basis function (rbf) neural networks acts better than other models in predicting the production performance of laying hens. so we can conclude rbf model performed better predict laying hen performance.