灰色神经网络在大学生就业信心指数预测中的应用

Application of Gray Neural Network in the College Students’ Employment Confidence Index Prediction

大学生的就业信心是一个值得研究的问题,采用大学生就业信心指数来分析并预测其就业信心具有现实意义.提出一种基于灰色理论和BP神经网络相结合的预测方法对大学生就业信心指数进行预测.该方法对影响大学生就业信心的主要因素建立不同的灰色模型,将每个灰色模型的预测值作为神经网络的输入,利用神经网络进行组合预测以作为其最终的预测值.结果表明组合模型的预测值相对误差更小,精度更高.

It is a valuable research issue to know and increase the college students＇ employment confidence. It has practical significance to analyzes and predict the employment confidence using the employment confidence index. In this paper, a method was presented to predict the college students＇ employment confidence index based on gray theory and BP neural network. This method set up different gray models based on the influence factors of employment confidence. And the forecasting results of different gray models are inputted to the neural network. The final forecasting results were obtained according to the neural network. The results show that the combined forecasting model has smaller relative error and higher prediction accuracy.