The eigenvalues of some liquid drop fingerprints are of high similarity, which decreases the recognition accuracy rates of BP neural network. In order to solve this problem, recognition method based on cluster analysis and BP neural network is proposed in this paper. Cluster analysis is used to classify liquid samples according to the similarity of eigenvalues and narrow the recognition range for samples under study. The experimental results have proved that this method is able to increase the recognition accuracy rate from 83.42% to 99.83%.
In this study, an Artificial Neural Network (ANN) model was developed in order to calculate the two-neutron separation energies (S2n) for the even-even nuclei 36-58Ca, 50-78Ni, 100-138Sn and 182-220Pb with the magic proton numbers, 20, 28, 50 and 82, respectively. The obtained results were compared with the Liquid Drop Model (LDM), Relativistic Mean Field Theory (RMFT) and the experimental results.
