ISSN 1003-8280 CN 10-1522/R 中国疾病预防控制中心 主办
Chinese Journal of Vector Biology and Control >
Application of Elman feedback neural network model to predict the incidence of hemorrhagic fever with renal syndrome
Received date: 2015-02-23
Online published: 2015-08-20
Supported by
Supported by the National Natural Science Foundation of China(No. 81202254, 30771860)
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.
WU Wei, GUO Jun-qiao, AN Shu-yi, GUAN Peng, ZHOU Bao-sen . Application of Elman feedback neural network model to predict the incidence of hemorrhagic fever with renal syndrome[J]. Chinese Journal of Vector Biology and Control, 2015 , 26(4) : 349 -352 . DOI: 10.11853/j.issn.1003.4692.2015.04.005
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