基于Elman反馈型神经网络的肾综合征出血热发病率预测模型  

Application of Elman feedback neural network model to predict the incidence of hemorrhagic fever with renal syndrome

作　　者：吴伟[1] 郭军巧[2] 安淑一[2] 关鹏[1] 周宝森[1] 

机构地区：[1]中国医科大学,辽宁沈阳110122 [2]辽宁省疾病预防控制中心

出　　处：《中国媒介生物学及控制杂志》2015年第4期349-352,共4页Chinese Journal of Vector Biology and Control

基　　金：国家自然科学基金(81202254;30771860)~~

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。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.

关 键 词：肾综合征出血热 ELMAN神经网络 发病率 预测 

分 类 号：R373.32[医药卫生—病原生物学]