Abstract:

Objective To describe the procedure of building Elman neural network model, and explore the value of potential application of the above model. Methods Monthly incidence of hemorrhagic fever with renal syndrome(HFRS) in China from 2004 to 2013 was used to build Elman neural network model and SARIMA model and forecasted the monthly incidence of HFRS in China from January 2014 to September 2014. The fitting and prediction effects of the two models were compared. Results For training sample, MAE, MAPE and RMSE of Elman neural network were 0.0088, 0.1191 and 0.0127 respectively; MAE, MAPE and RMSE of SARIMA model were 0.0111, 0.1268 and 0.0206 respectively. For predicting sample, MAE, RMSE and MAPE of Elman neural network were 0.0079, 0.1180 and 0.0096 respectively; MAE, RMSE and MAPE of SARIMA model were 0.0178, 0.2778 and 0.1861 respectively. Conclusion Elman neural network fits and forecasts the HFRS incidence trend in China well, and the fitting and prediction effect is superior to the SARIMA model, which is of great application value for the prevention and control of hemorrhagic fever with renal syndrome.

Key words: Hemorrhagic fever with renal syndrome, Elman neural network, Incidence, Predict

摘要：

目的 阐述建立Elman神经网络模型预测肾综合征出血热(HFRS)发病率的方法和步骤,探讨其应用前景。方法 使用全国2004－2013年HFRS的月发病率资料,建立Elman神经网络预测模型和SARIMA模型,对2014年1－9月HFRS的月发病率进行预测,比较2个模型的拟合和预测效果。结果 对于训练样本,Elman神经网络的平均绝对误差(MAE)、平均绝对误差百分比(MAPE)以及均方误差平方根(RMSE)分别为0.0088、0.1191和0.0127;SARIMA 模型的MAE、MAPE 和RMSE 分别为0.0111、0.1268 和0.0206。对于预测样本,Elman 神经网络的MAE、MAPE 和RMSE 分别为0.0079、0.1180 和0.0096;SARIMA 模型的MAE、MAPE 和RMSE 分别为0.0178、0.2778 和0.1861。结论 Elman神经网络较好地拟合和预测了全国HFRS的发病趋势,并且其拟合和预测效果优于SARIMA模型,具有较强的推广应用价值。

关键词: 肾综合征出血热, Elman神经网络, 发病率, 预测