神经网络在麻疹预测预警中的应用

Early warning on measles through the neural networks

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.

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.