NDLI: Equipment spare parts demand forecasting model based on grey neural network

Content Provider	IEEE Xplore Digital Library
Author	Hui Song Cheng Zhang Guangyu Liu Wukui Zhao
Copyright Year	2012
Description	Author affiliation: The 6th Department, Shijiazhuang Mechanical Engineering College, Hebei, China (Hui Song; Cheng Zhang; Guangyu Liu; Wukui Zhao)
Abstract	Equipment spare parts demand forecasting is the precondition of conducting effective spare parts supporting. Equipment spare parts demand change is the result of comprehensive factors and single model forecasting accuracy is not high. Aim to improve the precision of equipment spare parts demand forecasting, a forecasting method of equipment spare parts demand is proposed using grey neural network based on analyzing the main factors influencing spare parts wastage synthetically. The proposed method uses the grey forecasting model to train the training samples and gets the BP neural network input value, then BP neural network is used to get the equipment spare parts demand results. Simulation results demonstrate that the proposed method has higher forecasting precision compared with single forecasting model, which verifies the correctness and efficiency of the proposed method.
Starting Page	1274
Ending Page	1277
File Size	1324062
Page Count	4
File Format	PDF
ISBN	9781467307864
e-ISBN	9781467307888
DOI	10.1109/ICQR2MSE.2012.6246453
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Missiles Spare parts demand Neural networks Demand forecasting Predictive models Data models Mathematical model Gray neural network
Content Type	Text
Resource Type	Article

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