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- W2182378312 abstract "Artificial neural networks are mathematical models that have been inspired by the human nervous system and brain. In this study, the purpose is establishing a method to predict the cost of tractor repair and maintenance more accurately. In this paper, the multi-layer neural network with Feed Forward Backpropagation training algorithm (FFBP) has been used to predict repair and maintenance costs of tractor. In addition, 60 real data from two-wheel drive tractors existing in Razavi agro-industry in Iran have been used to train and test the network. The appropriate parameters for network training have been selected through error test on data. In this study, the performance of Backpropagate Declining Learning Rate Factor algorithm (BDLRF) has been compared with Feed-Forward Backpropagation algorithm (FFBP), with error criteria (MAPE, MSE, and RMSE) and the result shows that training Feed Forward Backpropagation algorithm (FFBP) surpasses the (BDLRF) algorithm in predicting tractor repair and maintenance costs by using separate networks rather than a single network." @default.
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- W2182378312 date "2013-01-01" @default.
- W2182378312 modified "2023-09-24" @default.
- W2182378312 title "Machinery Cost Prediction Based on a New Neural Network Method" @default.
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