Spectral analysis by a neural network using processed data whose dimensions have been reduced by principal component analysis as learning data

Oba, Masaki 

Dimension reduction was performed using PCA on 462 types of training data obtained by processing GdO, TiO, ZrOdata. After learning, the content rates of elements were analyzed using 62 types of data from actual samples as test data. Similar to last time, create a calibration curve of the true value and analytical value of the actual sample and analyze the content ratio. The neural network used this time had a configuration of input layer, middle layer (2 layers), and output layer, and the middle layer used 2 layers with 100 nodes each. As a result of PCA on the training data, we were able to significantly reduce the 7944 dimensions (pixels) of the training data to 5 dimensions. As a result of training using this and analyzing test data, the difference from the true value was approximately 10%, which was almost the same as the previous value.