Demand Prediction of the Rarely Used Spare Parts Based on the BP Neural Network

Abstract:

For the rarely used spare parts, as the traditional predicting methods can't keep the high accurateness, the BP neural network is used to predict the rarely used spare parts demand. Firstly, the rarely used spare parts definition and its characteristics are given in this paper. Then the three layer BP neural network model is established, the back propagation algorithm is used as the learning algorithm. Finally, the rarely used spare parts-bus coupler consumption data is used for simulation analysis based on Guangzhou Subway line 3. The results show that the prediction is good.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 1513-1519
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.1513
• Online since: February 2014
• Authors: Hong Long Mao, Jun Wei Gao, Xi Juan Chen, Jin Dong Gao
• Keywords: Demand Prediction, Neural Network (NN), Rarely Used Spare Parts

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