生猪市场价格风险预警的GRNN建模及其实证研究  

Forecasting the Market Price Risks of Live Pigs Applying a General Regression Neural Network and Its Empirical Research

作　　者：楼文高[1,2,3] 刘林静[2] 陈芳[2] 张博[3] 

机构地区：[1]上海商学院管理学院,上海200235 [2]上海理工大学光电工程与计算机学院,上海200093 [3]上海理工大学出版印刷与艺术设计学院,上海200093

出　　处：《数学的实践与认识》2017年第6期19-28,共10页Mathematics in Practice and Theory

基　　金：上海高校知识服务平台"上海商贸服务业知识服务中心"建设子项目"上海市价格监管和智能决策系统设计及开发"项目(ZF1226);上海市重点学科"商务经济学"项目资助

摘　　要：为了对生猪市场价格风险进行预警,根据我国2009年1月-2011年8月14个指标的32组样本数据,建立了广义回归神经网络(GRNN)预警模型,其中训练样本29组,测试样本3组.训练样本和测试样本的均方根误差、平均绝对误差(AAE)和相关系数都非常接近,说明建立的模型具有较强的泛化能力和鲁棒性,测试样本的AAE为0.0062,平均相对误差为2.3%,说明建立的GRNN模型具有很高的预测精度,可用于我国生猪市场价格风险预警研究和实际预测,并为政府有关部门指导生猪生产和进行市场调控提供决策依据.In order to forecast the market price risks of live pigs, the general regression neural network （GRNN） model was applied and established according to the thirty-two sample data of fourteen indexes during 2009.1-2011.8. Of the sample data, twenty-nine is training data set （TrDS）, and the other is testing data set （TeDS）. The values of the root-mean-square error （RMSE）, the absolute average error （AAE） and the relative coefficient R between the TrDS and the TeDS are very close, and show that the GRNN＇s generalization and robustness is quite good. The forecasted AAE of the TeDS - the market price risks of live pigs in three months, is less than 0.0062, and its mean absolute percentage error is less than 2.3%. These results show that the above-established GRNN model possesses good practicality, and it can be applied to forecast the market price risks of live pigs, administrate the live pigs＇ production, and adjust the market by the local government and its authorities.

关 键 词：广义回归神经网络 生猪 市场价格 风险预警 实证研究 

分 类 号：F323.7[经济管理—产业经济] TP183[自动化与计算机技术—控制理论与控制工程]