华东师范大学学报(自然科学版) ›› 2003, Vol. 2003 ›› Issue (4): 73-79.

• 论文 • 上一篇    下一篇

中国粮食生产的多元回归与神经网络预测比较

吴玉鸣12；徐建华1   

  1. 1 华东师范大学 教育部城市环境动态过程开放实验室，上海 200062； 2 广西师范大学 法商学院，桂林 541001
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2003-12-25 发布日期:2003-12-25

A Comparative Study on Muti-regression Analysis and Artificial Neural Network of Corn Production in China

WU Yu-ming12; XU Jian-hua1   

  1. 1 Urban ＆Enviromental Dynamics and Geocomputation,East China Normal University,Shanghai 200062,China 2 School of Law ＆Business , Gunagxi Normal University, Guilin 541001,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2003-12-25 Published:2003-12-25

摘要： 对1978-2000年影响我国粮食生产的7个因子分别建立了多元回归分析预测模型BP神经网络多变量输入预测模型。实证研究结果表明，与回归预测模型相比，用BP网络建立的模型经过训练后，可得到影响粮食产出的主要因子及其之间的非线性关系，网络模型新颖，具有很高的预测精度及较好的预测效果，可广泛应用于各种预测研究，有较高的推广价值。

关键词: 多元回归分析预测模型, BP神经网络模型, 粮食生产, 预测, 比较, 多元回归分析预测模型, BP神经网络模型, 粮食生产, 预测, 比较

Abstract: Aiming at the influencing variables and data from the year of 1978 to 2000 in China, this paper sets up muti-regression analysis model and BP artificial neural network model. Take prediction of total corn production as an example, two methods of corn production prediction based on multi-regression analysis and BP artificial neural network model are introduced in this paper. The results show that after being exercised,the network can provide nonlinear mapping relation between independent variables and dependent variable of corn production in China. The model is novelty, has higher precision and good effect.It can be widely applied in modeling of many forecast area,and also has high generalizing value.

Key words: muti-regression analysis, BP artificial neural network model, corn production, prediction, comparison, BP artificial neural network, muti-regression analysis, BP artificial neural network model, corn production, prediction, comparison