| 基于组合模型的农产品物价预测算法 |
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| 引用本文： | 苏照军,郭锐锋,高岑,王美吉,李冬梅.基于组合模型的农产品物价预测算法[J].计算机系统应用,2019,28(5):185-189. |
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| 作者姓名： | 苏照军 郭锐锋 高岑 王美吉 李冬梅 |
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| 作者单位： | 中国科学院大学 计算机控制与工程学院, 北京 100049;中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168 |
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| 摘 要： | 当今时代，科学技术高速发展，涌现出一批新技术，数据挖掘、机器学习等新科学领域被深入研究，众多智能算法逐渐出现，同时被应用到了不同的领域中.本文构建了一种基于BP （Back Propagation）神经网络和SVR （Support Vector Regression）支持向量回归机的组合模型.依托于农产品价格数据进行实例验证分析，结果表明相对于单一的预测模型，BP-SVR-BP组合模型在预测精度上有了很大的提升，拟合效果更加逼近真实数据曲线，能够客观真实的反应农产品物价变化规律.
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| 关 键 词： | 组合模型 BP神经网络 物价预测 SVR预测 农产品 |
| 收稿时间： | 2018/12/4 0:00:00 |
| 修稿时间： | 2018/12/26 0:00:00 |
Agricultural Product Price Forecasting Algorithm Based on Combination Model |
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| SU Zhao-Jun,GUO Rui-Feng,GAO Cen,WANG Mei-Ji and LI Dong-Mei.Agricultural Product Price Forecasting Algorithm Based on Combination Model[J].Computer Systems& Applications,2019,28(5):185-189. |
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| Authors: | SU Zhao-Jun GUO Rui-Feng GAO Cen WANG Mei-Ji and LI Dong-Mei |
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| Affiliation: | School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China and Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China |
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| Abstract: | Nowadays, with the rapid development of science and technology, a number of new technologies have emerged. New scientific fields such as data mining and machine learning have been deeply studied. Many intelligent algorithms have emerged and applied to different fields. This paper constructs a combined model based on BP (Back Propagation) neural network and SVR (Support Vector Regression). Based on the agricultural product price data, the example verification analysis shows that compared with the single prediction model, the BP-SVR-BP prediction model has greatly improved the prediction accuracy. The fitting effect is closer to the real data curve, which can objectively and truly reflect the law of agricultural product price changes. |
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| Keywords: | combined model BP neural network price forecasting SVR forecasting agricultural products |
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