摘要
提出一种针对我国高校教师职称评审的预测模型。该模型通过将灰关联分析与BP神经网络相结合的方式实现,利用灰关联分析找出高校教师职称评审的各影响因子与职称晋升的潜在关系,为BP神经网络提供筛选输入因子的功能,最后通过训练BP神经网络来实现预测。选用某高校2012年副教授评审实际数据作为评价样本,将原有的6-8-2的网络结构简化为5-8-2,结果表明,建立的评审模型的结论优于基于BP神经网络的结论,且训练效率也有大幅提高,有一定的推广应用价值。
This paper presented a professional title forecast model implemented by gray relational analysis combined with BP neural network, using gray relational analysis to find out potential relationship between impact factor and professional title promotion,to provide screening function of the input factors for the BP neural network, and finally through the training of BP neural network to achieve the forecast. Taking 46 teachers＇ promotion to associate professor in the Institution in 2012 as assessment sample, 6-8-2 network model was simplified to5-8-2 network model.The result showed that the results obtained by the established assessment model were completely consistent with the results obtained by the established assessment model were completely consistent with the simulation results based on artificial neural net-work, the training efficiency of the model is raised greatly, so the model has a value to be applied in certain extent.
出处
《价值工程》
2013年第6期170-171,共2页
Value Engineering
关键词
职称评审
灰关联分析
BP网络
professonal titles evaluation
GRAY
BP network
