Abstract
This study was carried out to investigate the ability of major mathematical methods to estimate intestinal broiler microflora population. Artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIC), and artificial neural network genetic algorithm (ANNGA) were used in this respect. The lactic acid bacteria and Enterobacteriaceae were applied as models of microflora. Input and output variables were considered as time and microflora population, respectively. The best model of ANN, CANFIC, and ANNGA was determined based on the coefficient of determination and root mean square error criteria. The results of the current study have shown that ANN, ANNGA, and CANFIS are accurate methods to estimate lactic acid bacteria and Enterobacteriaceae. The highest accuracy of microflora estimation was related to 7 days of age. The efficiency of intelligent models to lactic acid bacteria and Enterobacteriaceae has shown that ANNGA had better prediction between mentioned models. The models estimated Enterobacteriaceae population better than that for lactic acid bacteria.
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Acknowledgments
Our special thanks to Tarbiat Modares University for providing facilities and financial support for this study. We also wish to thank the Saeed Yakhkeshi for their excellent collaboration.
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Hemati Matin, H.R., Saki, A.A., Bayat Varkeshi, M. et al. Comparison and validation of artificial intelligent techniques to estimate intestinal broiler microflora. Neural Comput & Applic 23, 61–66 (2013). https://doi.org/10.1007/s00521-012-1059-2
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DOI: https://doi.org/10.1007/s00521-012-1059-2