APPLICATION OF THE ARTIFICIAL NEURAL-NETWORK APPROACH TO THE RECOGNITION OF SPECIFIC PATTERNS IN AUGER ELECTRON-SPECTROSCOPY

The artificial neural network (ANN) approach was applied to the identi fication of Auger electron spectral patterns. The ANN structure employ ed was the counter-propagation architecture with an unsupervised learn ing algorithm. For training such a network, it is only necessary to pr ovide a data set with samples of the patterns to be recognized, and th e network itself will extract the relevant statistical information to organize similar patterns into specific classes. We used a training da ta set of five different Auger spectra (Fe, Au, Si, Sn, Cu) to which a random fluctuation of up to 5% of the highest peak was added. To the test set, however, the added fluctuation was up to 50% and we observed that the network was able to identify precisely any test spectrum aft er only a few training sessions. The ANN synapses can be interpreted a s the average spectra of the training set for each specific class, ten ding to zero fluctuation spectra as the number of training samples bec omes large. The results obtained show that even by using an extremely simple ANN structure the classification of single-element Auger spectr a was made easy also in the case of extremely noisy spectra.