Prediction of Yield Strength of Reinforced Using BP Network Based on Partial Least Squares
JING Lin-lin1,YUAN Shou-qian1,LI Du-hong2
1. Xi��an University of Architecture and Technology, Xi��an 710055, Shaanxi China 2. Shaanxi Steel Group Hanzhong Iron and Steel Company, Hanzhong 724200, Shaanxi China
Abstract��Combining with the partial least squares and artificial network , a new prediction model is established��PLS-BP neural network model. Partial least squares regression is applied to extract principal components R and score T, according to the cross validation and left N method, PLS component number, input of the PLS-BP neural network and the number of hidden layer nodes of network is determined, ultimately is the network structure for 6-11-1. Using the model, factors between multiple correlations can be effectively avoided; at the same time, the non-linear problem can be solved better and partial least squares and simple BP network faults can be overcome. The yield strength of steel projections shows that the error predicted by application of PLS-BP model is less than 1.03%; it is much smaller than the partial least squares regression error of 6.19% and coincides with the actual value.
�����գ�Ԭ��ǫ�����. ����ƫ��С���˷���BP����Ԥ��ֽ������ǿ��[J]. �й������ڿ���, 2012, 30(6): 59-62.
JING Lin-lin1,YUAN Shou-qian1,LI Du-hong2. Prediction of Yield Strength of Reinforced Using BP Network Based on Partial Least Squares. Chinese Journal of Iron and Steel, 2012, 30(6): 59-62.
2012, Vol. 30 
