基于BP神经网络和遗传算法的肺结核发病率预测模型研究

Prediction Model of Pulmonary Tuberculosis Incidence Based on GA-BP Neural Network

对肺结核发病率进行逼近准确的预测,有助于卫生行政等政府部门在肺结核防控规划中提出适宜的目标。文章利用遗传算法对BP神经网络进行优化,建立基于BP神经网络和遗传算法的肺结核发病率预测模型,用来预测肺结核发病率。实验结果表明,模型具有良好的自我学习和预测的能力,以及全局寻优和快速收敛等特性,可以对肺结核发病率进行逼近准确的预测。

The prediction of pulmonary tuberculosis incidence,helped government departments to make appropriate goals for tub- erculosis prevention planning.This paper built prediction model of pulmonary tuberculosis incidence based on GA-BP neural network to predict the incidence of pulmonary tuberculosis.Experimental results showed that the prediction modelcan predict pulmonary tuberculosis incidence approximate accurately.