基于BP神经网络与灰色预测理论的人口死亡率预测

An Prediction of Population Mortality Based on BP Neural Network Model and Grey Prediction Theory

根据2013年《福建统计年鉴》上收集到的资料,运用GM(1,1)模型以及MATLAB构建3层BP神经网络结构模型,分别对福建省人口死亡率进行建模预测。结果表明:BP神经网络预测人口死亡率拟合结果优于GM(1,1)模型,最小相对误差率为1.05%,最大相对误差率为3.75%,平均相对误差率为2.54%,拟合结果显示模型可靠。BP神经网络非线性结构系统,具有预测精度高,可行性强的特点,表明BP神经网络可用于未来人口规模预测,为福建省经济社会发展战略与规划,调整人口结构,制定合理的人口决策提供参考。

According to data collected from Fujian statistical yearbook in 2013, using GM （1, 1） model and a three-layer BP neural network struc ture model build by MATLAB to predict population mortality of Fujian province respectively. Result shows that BP neural network has an better fitting result in population mortality predicting than GM （1, 1） model, which minimum relative error is 1.05%, maximum relative error is 3.75 %, and average relative error is 2.54 %, this fitting result shows that model is reliable. BP neural network, which contains an nonlinear structure system, has higher prediction accuracy and stronger feasibility characteristics, so BP neural network could be used to forecast future population scale, and providing reference of economic and social development strategy planning and adjusting structure of population , making reasonable pop- ulation decisions for Fujian province.