摘要
采用大学生就业信心指数来分析预测其就业信心是值得研究的问题.提出一种灰色理论和BP神经网络相结合的方法对大学生就业信心指数进行预测.首先对影响就业信心的主要因素建立不同的灰色模型,然后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 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 firstly set up different gray models based on the influence factors of employment confidence. And then 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.
出处
《数学的实践与认识》
CSCD
北大核心
2013年第13期62-67,共6页
Mathematics in Practice and Theory
关键词
就业信心指数
灰色理论
神经网络
employment confidence index
gray theory
neural network
