【摘要】 针对传统学生英语写作成绩预测方法准确率偏低的情况,提出一种基于主成分分析(PCA)和BP神经网络相结合的写作成绩预测模型。首先,用PCA对所建立的学生写作评价体系作数据降维处理,提取前3个主成分,构建了新的样本矩阵,再对BP神经网络进行训练和泛化能力测试。仿真结果表明:单一的BPNN预测最大相对误差为-2.165%,PCA-BPNN预测最大相对误差仅为-0.824 2%,PCA-BPNN简化了网络结构,提高了单一BPNN的训练速率、预测精度和泛化能力,验证了所提出的模型的有效性。更多还原

【Abstract】 In view of the low accuracy of traditional prediction method of students’ English writing scores,aprediction model based on principal component analysis(PCA)and BP neural network was proposed.First,the dimensions of the evaluation system of students’ writings were reduced by PCA.The first three principal components were extracted to create a new sample matrix.Then the BP neural network was trained and its generalization ability was tested.The simulation results show that the maximum relative error of prediction produced by the simple BPNN is-2.165%,while the one produced by the PCA-BPNN is only-0.824 2%.The PCA-BPNN simplifies the network structure.It also improves the training rate,prediction accuracy and generalization ability of the simple BPNN.The effectiveness of the proposed model is verified.更多还原