Abstract
Aiming at the problem that the consuming-related factors of military aircrafts spare parts cant be revealed in the model, support vector machines (SVM) model was applied in the consuming prediction of spare parts. In the model, the main factors that affected spare parts consumption were taken as the input of SVM while the output was the consumption. Then, the test samples were input the trained model for prediction. The results show that, compared with GM (1,1) model and neural network model (ANN), the model has higher prediction accuracy and dynamic adaptability, which can provide some reference for the spare parts management sections.
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