基于Verhulst-BP模型的家庭经济困难学生预测的研究

摘要

为了掌握高校家庭经济困难学生数的变化趋势，从而帮助高校乃至教育行政部门制定资助政策的正确决策，本文通过运用灰色Verhulst 模型与BP 神经网络相结合的方法来预测这一数据的变化。实例证明，单独使用这两个模型，预测值的平均相对误差率均大于组合模型，因此，组合模型精度准确性较高，有一定的应用价值。%In order to grasp the trend of the number of university students from poor families, to help colleges and universi-ties as well as educational and administrative departments to make the right decisions subsidy policy, the paper through the use of gray Verhulst model and BP neural network combined method to predict changes in the data. Examples prove that the use of these two models alone, the average relative error rate is greater than the combined model predicted values, and there-fore, higher precision accuracy combined model, there is a certain value.