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Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach

Sefeedpari, Paria, Rafiee, Shahin, Akram, Asadollah, Chau, Kwok-wing, Pishgar-Komleh, Seyyed Hassan
Computers and electronics in agriculture 2016 v.131 pp. 10-19
egg production, eggs, electricity, energy, fuels, labor, neural networks, poultry industry, pullets
Adaptive neural fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of uncertain systems. In this paper, we proposed an ANFIS based modeling approach (called MLANFIS) where the number of data pairs employed for training was adjusted by application of clustering method. By employing this method, the number of data required for learning step and thereby its complexity were significantly reduced. The results obtained were compared with those obtained by using artificial neural networks (ANNs). Inputs to the first group were feed supply, fuel and machinery and the ones to second cluster were pullet, electricity and labor energies. Finally, the outputs of aforementioned networks were considered as inputs to ANFIS 3 network and predicted values of egg yield were derived. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) parameters of ANFIS 3 network were calculated as 0.92, 448.126, 0.014, respectively showing that ANFIS compared with ANNs with statistical parameters as 0.81, 751.96 and 0.019 respectively, can properly predict the egg yield of poultry farms. As a recommendation for future studies, ANFIS models with multi-layered structures can be developed to find the optimum number of layers.