Grey -Neural Network Combination Forecast Model of the World Food Consumption

摘要：

This paper puts forward a world food consumption forecast method, which is based on grey -neural network combination forecast model. Firstly, we make predictions according to the original data by using GM(1,1) and BP neural network respectively. Then we introduce proper weights and establish the grey - neural network combination forecast model. Finally, we get the results. Example proves that the method can raise forecast accuracy effectively and is a very effective and much more accurate grain consumption forecast model.

关键词： grain consumption GM(1,1) BP neural network weighted coefficient combination forecast

作者: Jiehao Wang Yan Xing, Feihu Qin Tianran Ma Haonan Liang

作者单位: School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou,China

会议类型: 国际会议

会议名称: 2010 International Conference on Information,Networking and Automation(2010 IEEE信息网络与自动化国际会议 ICINA 2010)

会议地点: 昆明

会议语种:英文

页码: 129-132

在线出版日期: 2010-10-17（万方平台首次上网日期，不代表论文的发表时间）