CAJ | 학술논문

为了进一步提高教学质量评价准确率，提出一种基于主成分分析（PCA）和学习矢量量化神经网络（LVQ）相结合的教学质量评价模型。使用层次分析法（AHP）建立教学质量评价体系，再用主成分分析提取初始评价指标体系的特征信息，将经过降维处理后的特征信息输入到LVQ神经网络，并对网络模型进行训练和泛化能力测试。实验结果表明，与单一的LVQ和BP神经网络相比，PCA-LVQ网络模型的结构更为简化，学习能力更强，收敛速度更快，评价准确率更高且泛化能力强。
위료진일보제고교학질량평개준학솔，제출일충기우주성분분석（PCA）화학습시량양화신경망락（LVQ）상결합적교학질량평개모형。사용층차분석법（AHP）건립교학질량평개체계，재용주성분분석제취초시평개지표체계적특정신식，장경과강유처리후적특정신식수입도LVQ신경망락，병대망락모형진행훈련화범화능력측시。실험결과표명，여단일적LVQ화BP신경망락상비，PCA-LVQ망락모형적결구경위간화，학습능력경강，수렴속도경쾌，평개준학솔경고차범화능력강。
To improve the accuracy of teaching quality assessment,an assessment model based on Principal Component Analysis(PCA)and Learning Vector Quantization(LVQ)is proposed. A teaching quality assessment system is established using Analytic Hierarchy Process(AHP),and characteristic information of indexes in the initial assessment system is extracted by PCA. The characteristic information is put into the LVQ neural network after dimension reduction. The network model is trained and its generalization ability is also tested. The experiment result shows that ,compared with simple LVQ neural network and simple BP neural network,PCA-LVQ network model has simpler network structure,better learning ability,faster convergence speed,higher accuracy and better generalization ability.