Prediction of Tractor Repair and Maintenance Costs Using RBF Neural Network

In this article the potential of Radial Basis Function Neural Network (RBFNN) technique has evaluated as an alternative method for the prediction of tractor repair and maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghodse Razavi agro-industry in Iran. In this paper, the performance of Basic Back-propagation (BB) training algorithm was also compared with Back-propagation with Declining Learning Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of tractor's costs. It has been concluded that RBFNN represents a promising tool for predicting repair and maintenance costs.