关键词: 广义回归神经网络, 反馈神经网络, 肾综合征出血热, 预测

Abstract: Objective To study the superiority and application prospect of generalized regression neural network(GRNN) which is used in forecasting the incidence of hemorrhagic fever with renal syndrome(HFRS). Methods Use meteorological data, including average temperature, relative humidity, precipitation and sunshine time, and epidemiologic information of animal diseases, including rodent density and viral carriage of rodents from 1984 to 2002 as the input of neural network. Use the incidence of HFRS from 1985 to 2003 as the output of neural network. Construct the GRNN forecasting model and BP neural network forecasting model respectively with the neural network toolbox of Matlab7.0. Fit and forecast the sample and compare the performance between the two different neural networks. Results The optimize smooth factor of GRNN is 0.35; the hidden layers of BP neural network is 6. From the fitting effect, the MER of GRNN and BP neural network are 25.42% and 25.55% respectively; their r² are 0.9438 and 0.9729. On the whole, the fitting effect is satisfactory, and the difference of the two neural networks is not very significant. From the forecasting effect, the MER between the two neural networks are 4.90% and 15.16% respectively. The MER of GRNN is less than the MER of BP neural network; their r² are 0.9897 and 0.9516. Conclusion GRNN is more superior in small sample forecasting than BP neural network, and the forecasting effect is better. GRNN has practical value in solving epidemic problem which has complicated influencing factor such as HFRS.