基于时间序列的小波神经网络蔬菜价格预测模型  

Vegetable Prices Prediction Model Based on Time-Series Wavelet Neural Network

作　　者：钱彬彬[1] 谢申汝 杨宝华[1] 

机构地区：[1]安徽农业大学信息与计算机学院,安徽合肥230036

出　　处：《洛阳理工学院学报（自然科学版）》2016年第4期63-67,共5页Journal of Luoyang Institute of Science and Technology：Natural Science Edition

基　　金：安徽省高校自然科学研究项目:基于地空遥感耦合的作物生物量协同反演研究(KJ2016A837)

摘　　要：为准确预测蔬菜价格变化规律,现以合肥市周谷堆农产品批发市场2005年~2014年白菜月度市场价格及相关影响因素数据为样本,通过主成分分析,利用小波神经网络智能分析方法,构建基于小波神经网络的价格预测模型,并与BP神经网络模型比较。结果表明,小波神经网络预测模型的预测精度比BP神经网络更高,且更加的稳定。该模型的构建对蔬菜价格的稳定、农业管理部门的决策支持具有重要的理论研究意义和实际价值。Aiming at an accurate prediction of vegetable price, the cabbage monthly price data and the relevant factors data in Hefei Zhou Gudui farm product market from 2005 to 2014 were collected as samples. By analyzing principal component and using the Wavelet Neural Network intelligent analysis method, the price forecasting model was built and was compared with BP neural network model. The results show that the wavelet neural network predictive model has higher precision and better stability than the BP neural network. The establishment of the model would provide technical support for stability of the price and references for related policies.

关 键 词：蔬菜价格 主成分分析 小波神经网络 预测模型 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] F304.2[自动化与计算机技术—控制科学与工程]