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Proceedings Paper

Machine recognition of atomic and molecular species using artificial neural networks
Author(s): Arthur L. Sumner; Steven K. Rogers; Gregory L. Tarr; Matthew Kabrisky; David Norman
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Paper Abstract

Spectral analysis involving the determination of atomic and molecular species present in a spectm of multi—spectral data is a very time consulTLLng task, especially considering the fact that there are typically thousands of spectra collected during each experiment. Ixie to the overwhelming amount of available spectral data and the time required to analyze these data, a robust autorratic 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 backpropagation perceptron. The obj ective 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.

Paper Details

Date Published: 1 August 1990
PDF: 8 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21164
Show Author Affiliations
Arthur L. Sumner, U.S. Air Force Institute of Te (United States)
Steven K. Rogers, U.S. Air Force Institute of Te (United States)
Gregory L. Tarr, U.S. Air Force Institute of Te (United States)
Matthew Kabrisky, U.S. Air Force Institute of Te (United States)
David Norman, U.S. Air Force Institute of Te (United States)

Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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