摘要
当前,教育大数据呈现数据量大和数据类型多样的特点,准确有效地对教育统计数据进行分析和预测,对教育部门相关政策的制定和社会的发展具有重要的参考价值。文中以某市每年的招生人数为数据基础,提出了DE-LSTM模型,该模型通过差分进化算法(DE)对长短期记忆神经网络(LSTM)中的隐含层节点和学习率进行优化,使所提模型具有较好的预测性能,并与现有的BP神经网络预测模型、LSTM神经网络预测模型进行了对比。实验结果表明,提出的DE-LSTM预测模型具有较高的预测精度。
At present,educational data presents the characteristics of large amount of data and diverse data types.Accurate and effective analysis and prediction of educational statistical data,which has important reference value for the formulation of relevant policies in education sector and social development.In this paper,DE-LSTM model is proposed,which takes the annual enrollment of a city as the data basis.The proposed model uses differential evolution algorithm to optimize the hidden layer nodes and lear-ning rate in the long-term and short-term memory neural network and has the better prediction performance in compared with the LSTM and BP models.Furthermore,effectiveness of the proposed DE-LSTM model is verified by a large number of simulation experiments.
作者
刘宝宝
杨菁菁
陶露
王贺应
LIU Bao-bao;YANG Jing-jing;TAO Lu;WANG He-ying(School of Computer Science,Xi'an Engineering University,Xi'an 710000,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S01期261-266,共6页
Computer Science
基金
陕西省教育厅信息保障专项科学研究计划项目(20JX004)
陕西省自然科学基础研究计划一般项目(面上)(2020JM-574)。
关键词
教育统计数据
时间序列预测
BP神经网络
长短时期记忆网络
差分进化算法
Education Statistics
Time series prediction
BP neural network
Long and short term memory network
Differential evolution algorithm
