Bitte wählen Sie ihr Lieferland und ihre Kundengruppe
Spectral analysis involving the determination of atomic and molecular species present in a spectrum of multi-spectral data is a very time consuming task, especially considering the fact that there are typically thousands of spectra collected during each experiment. Due to the overwhelming amount of available spectral data and the time required to analyze these data, a robust automatic method for doing at least some preliminary spectral analysis is needed. This research focused on the development of a supervised artificial neural network with error correction learning, specifically a three-layer feed-forward back-propagation perceptron. The objective was to develop a neural network which would do the preliminary spectral analysis and save the analysts from the task of reviewing thousands of spectral frames. The input to the network is raw spectral data with the output consisting of the classification of both atomic and molecular species in the source.