计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 261-266.doi: 10.11896/jsjkx.220300120
刘宝宝, 杨菁菁, 陶露, 王贺应
LIU Bao-bao, YANG Jing-jing, TAO Lu, WANG He-ying
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。
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