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为了提高教学质量评价的准确性,提出一种基于灰色理论和BP神经网络相融合的教学质量评价方法,研究首先确定对评价结果有重要贡献率的13个指标作为灰色神经网络输入向量,然后在BP神经网络中引入灰色模型构建灰色神经网络模型,最后通过发放问卷的形式收集课程的30个样本数据并训练灰色神经网络,利用训练后的神经网络评价测试样本及数据。结果表明:灰色理论的神经网络模型误差均方差是0.92,小于BP神经网络模型,可以有效评价课程的教学质量。
In order to improve the accuracy of teaching quality evaluation, a teaching quality evaluation method based on the combination of gray theory and BP neural network is proposed. First, the 13 indexes that have an important contribution to the evaluation result are determined as gray neural network input vectors. The gray neural network model is built by introducing the gray model into the BP neural network. Finally, 30 sample data of the course are collected through the questionnaire distribution and the gray neural network is trained. The trained neural network is used to evaluate the test sample and data. The results show that the mean square error of the gray theory neural network model is 0.92, which is smaller than the BP neural network model, which can effectively evaluate the teaching quality of the course.