Radial Basis Function Neural Network Model Applied in the Forecast of Population Aging——Taking Hunan Province as an Example
利用径向基函数神经网络，基于经济水平、自然人口增长和社会保障三个方面的历史数据，构建了湖南省人口老龄化预测模型，并与多元线性回归对比，发现该模型更准确可靠。
Artificial neural network has a good nonlinear mapping approximation performance,and it has been widely applied in all kinds of prediction.Radial Basis Function(RBF) is comparatively fast in network learning speed and able to avoid local minima,so its predictive value is more close to the true one.Aiming at the outstanding of Hunan population aging,based on the aging index historical data,this paper constructed an impact factor system from three aspects such as economic level,the natural population growth and social security.It also constructed a quantitative population aging prediction model by RBF neural network model.In contrast,this paper adopted multiple linear regression method to predict too.The result showed that the RBF neural network model was more accurate and reliable.