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Group Method of Data Handling-Type Neural Network Prediction of Broiler Performance Based on Dietary Metabolizable Energy, Methionine, and Lysine

H. Ahmadi^*, M. Mottaghitalab^*,1 and N. Nariman-Zadeh

^* Department of Animal Science, Faculty of Agriculture, University of Guilan, PO Box 41635-1314, Rasht, Iran; and Department of Mechanical Engineering, Faculty of Engineering, University of Guilan, PO Box 41635-3756, Rasht, Iran

Correspondence: ¹ Corresponding author: mottaghi2002{at}yahoo.co.uk

Artificial neural networks have been shown to be powerful tools for system modeling. One submodel of artificial neural networks is the group method of data handling-type neural networks (GMDH-type NN). The use of such self-organizing networks leads to successful application in a broad range of areas. However, in some fields, such as poultry science, the use of GMDH-type NN is still scarce. Broiler nutrition is recognized as a biological system consisting of a complex set of interconnected variables. Knowledge of an adequate description of variables, such as broiler ME and amino acid requirements, can help in establishing specific feeding programs, defining optimal performance, and reducing production costs. In this way, a genetic algorithm is deployed in a new approach to design the whole architecture of the GMDH-type NN (i.e., the number of neurons in each hidden layer and the configuration of their connectivities). This study addressed the question of whether GMDH-type NN could be used to estimate broiler performance (outputs) based on specified variables—inputs (level of dietary ME, Met, and Lys)—on a broiler farm. Results suggest that GMDH-type NN provide an effective means of efficiently recognizing the patterns in data and accurately predicting a performance index based on investigating inputs, and also can be used to optimize broiler performance based on nutritional factors.

Key Words: broiler performance index • nutritional factor • modeling • neural network

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