关键词: 因子分析法, 多元线性回归, 反向传播（BP）神经网络, 理化指标, 泛化能力

Abstract: In order to determine the independent variables of multiple linear regression and the input layer neurons of BP network, factor analysis is used to select out the 12 physical and chemical indicators with much impact on quality of wine as their independent variables and input layer neurons, respectively. Two models are established by using multiple linear regression and improved BP neural network, respectively, which show the relationships between the physical-chemical indicators and the quality of wine. The comparison of generalization performance for the both models, draws that average relative error of multiple linear regression model for the prediction of new samples is 1.93%, while the average relative error of the BP neural network model is 0.37%. The simulations show that the generalization capability and stability of the BP neural network are better than those of the multiple regression model.

Key words: factor analysis, multiple linear regression, Back Propagation（BP） neural network, physical and chemical indicator, generalization performance