@inproceedings{12305b1a282d460ba61b001cf4cfceb2,
title = "Agricultural machinery spare parts demand forecast based on BP neural network",
abstract = "With the rapid development of agricultural machinery, forecasting the demand for spare parts is essential to ensure timely maintenance of agricultural machinery. Based on features of spare parts, BP neural network is chosen to forecast the demand. First, this paper analyzes factors that affect the demand for spare parts. Second, steps and processes of neural network prediction are described. The third part of this paper is case study based on certain brand of agricultural machinery spare parts. BP neural network turns out suitable for forecasting the demand for spare parts.",
keywords = "Agricultural machinery, Demand Forecast, Neural Networks, Spare parts",
author = "Hu, {Yao Guang} and Shuo Sun and Wen, {Jing Qian}",
note = "Publisher Copyright: {\textcopyright} (2014) Trans Tech Publications, Switzerland.; 4th International Conference on Advanced Design and Manufacturing Engineering, ADME 2014 ; Conference date: 26-07-2014 Through 27-07-2014",
year = "2014",
doi = "10.4028/www.scientific.net/AMM.635-637.1822",
language = "English",
series = "Applied Mechanics and Materials",
publisher = "Trans Tech Publications Ltd.",
pages = "1822--1825",
editor = "Jianzhong Lin and Tianhong Yan and Xinsheng Xu and Zhengyi Jiang",
booktitle = "Advanced Design and Manufacturing Technology IV",
address = "Switzerland",
}