CAJ | 학술논문

探讨神经网络在麻疹发病数预警中的应用,以武汉市1986年1月至2006年8月麻疹病例数为依据,分别以月和周为单位,应用神经网络建立模型进行预测.结果表明,在用两层反向传播(BP)神经网络建立时间序列动态模型时,当p=9时,网络的收敛速度可以接受,相关系数达到0.85,接近l,按月预测较准确;用概率神经网络(PNN)按周分类预测较好.数据积累较多时,用两层BP神经网络预测作早期预警较为可行;对于某些疾病因数据的积累时间较短,利用时间序列精确预测不能得到令人满意的结果时,可以用PNN预测.该方法有望在疾病暴发早期预警系统的建立中得到应用.
탐토신경망락재마진발병수예경중적응용,이무한시1986년1월지2006년8월마진병례수위의거,분별이월화주위단위,응용신경망락건립모형진행예측.결과 표명,재용량층반향전파(BP)신경망락건립시간서렬동태모형시,당p=9시,망락적수렴속도가이접수,상관계수체도0.85,접근l,안월예측교준학;용개솔신경망락(PNN)안주분류예측교호.수거적루교다시,용량층BP신경망락예측작조기예경교위가행;대우모사질병인수거적적루시간교단,이용시간서렬정학예측불능득도령인만의적결과시,가이용PNN예측.해방법유망재질병폭발조기예경계통적건립중득도응용.
To discuss the effects on early warning of measles, using the neural networks.Based on the available data through monthly and weekly reports on measles from January 1986 to August 2006 in Wuhan city. The modal was developed using the neural networks to predict and analyze the prevalence and incidence of measles. When the dynamic time series modal was established with back propagation(BP) networks consisting of two layers, if p was assigned as 9, the convergence speed was acceptable and the correlation coefficient was equal to 0.85. It was more acceptable for monthly forecasting the specific value, but better for weekly forecasting the classification under probabilistic neural networks (PNN). When data was big enough to serve the purpose, it seemed more feasible for early warning using the two-layer BP networks. However, when data was not enough, then PNN could be used for the purpose of prediction. This method seemed feasible to be used in the system for early warning.