| 线性回归与BP神经网络方法的山东粮食产量预测比较研究 |
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| 引用本文： | 高亮亮,潘彩霞,屠星月.线性回归与BP神经网络方法的山东粮食产量预测比较研究[J].安徽农业科学,2014(30):10780-10783. |
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| 作者姓名： | 高亮亮 潘彩霞 屠星月 |
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| 作者单位： | 1. 农业部农业信息获取技术重点实验室,北京100083;山东农业大学信息学院,山东泰安271018
2. 农业部农业信息获取技术重点实验室,北京100083;中国农业大学信息与电气工程学院,北京100083 |
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| 摘 要： | 山东省是我国传统农业大省,粮食产量对我国粮食总产量的影响较大,因此对山东省粮食产量进行预测具有重大意义.分别利用多元线性回归方法和BP神经网络两种预测方法对山东粮食产量进行预测,并对两种方法的预测结果进行分析比较,实验证明,BP神经网络平均预测精度高于多元线性回归模型,且各期预测精度较多元线性回归模型更稳定,但随时间推移,误差增大,因此BP神经网络预测模型较适用于近期粮食产量预测.
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| 关 键 词： | 线性回归 BP神经网络 粮食产量预测 |
Comparison and Research of Linear Regression and BPNN for Shandong Province Grain Production Prediction |
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| GAO Liang-liang,PAN Cai-xia,TU Xing-yue.Comparison and Research of Linear Regression and BPNN for Shandong Province Grain Production Prediction[J].Journal of Anhui Agricultural Sciences,2014(30):10780-10783. |
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| Authors: | GAO Liang-liang PAN Cai-xia TU Xing-yue |
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| Institution: | GAO Liang-liang , PAN Cai-xia , TU Xing-yue ( 1. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083; 2. College of Information Science and Engineering, Shandong Agricultural University, Tai＇ an, Shandong 271018; 3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083 ) |
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| Abstract: | Shandong Province is a traditional agricultural province, of which grain production accounts for a high share of China＇s grain production. Therefore, grain production prediction of Shandong Province is of great importance. In this paper, linear regression and BP neural network are adopted for grain production forecast of Shandong Province, and a result compare between the two models is analyzed showing that BP neural network achieves higher average accuracy than the linear regression, and keeps more stable period accuracy. |
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| Keywords: | Linear regression BP neural network Grain production prediction |
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