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- W2189253003 abstract "This research is done in Mahabad Township to estimate the amount of energy consumption for sugar beet and to compare two models, neural network with regression models. Required information is obtained randomly by completing questionnaires and face to face interviews with 95 farmers. Results show that the fertilizer, seed, and herbicide were the major energy consumers, and minor energy consumers were machinery depreciation and manufacturing. Energy productivity, net energy gain, and energy ratio were respectively 1.02 kg/MJ, 189238.6 MJ and 3.9. To estimate output energy MLP, RBF, SOM, GFFN networks by changing in the number of hidden layers, training algorithm and number of neurons were used. Results showed that, MLP network have the maximum determination coefficient of 98% and the minimum MSE of 0.001 with topology of 55-1 and LM training. Exponential model was the best model, among linear, quadratic, cubic and exponential functions. Coefficient of determination was estimated 94.5% and it has less accuracy rather than MLP model." @default.
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- W2189253003 date "2012-01-01" @default.
- W2189253003 modified "2023-09-26" @default.
- W2189253003 title "Comparison among the Predictions of Energy Consumption for Sugar Beet by Using Artificial Neural Network and Regression Models in Iran" @default.
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