Classification of Cm I energy levels using PCA-BPN and PCA-NLM

Two pattern recognition (PR) techniques, principal component analysis-back propagation networks (PCA-BPN) and principal component analysis-nonlinear mapping (PCA-NLM), have been applied to the problem of classifying unknown energy levels of the first spectrum of curium (Cm I) according to their configurations. In comparison, with those reported by early PR techniques and counter propagation neural networks (CPN's), PCA-BPN has been demonstrated to possess much more prediction accuracy as to its performance on test sets. Obtained results further confirm the most previous assignments with these energy levels given by some early PR techniques and CPN. Moreover, the obtained results definitely reassign some energy levels' electronic configurations which were ambiguously conjectured in previous work.