ISSN:
1089-7623
Source:
AIP Digital Archive
Topics:
Physics
,
Electrical Engineering, Measurement and Control Technology
Notes:
Modern diagnostic instrumentation produces a vast amount of data that often requires substantial analysis efforts. New methods are needed to improve the efficiency of the analysis process. Artificial neural networks have been applied to a variety of signal processing and image recognition problems. The feed-forward, back-propagation technique is well suited for the analysis of scientific laboratory data, which is viewed as a pattern-matching problem. We summarize the concepts and algorithms as implemented on a personal computer, and illustrate the method using a nonlocal thermodynamic equilibrium theoretical atomic physics model for k-shell x-ray spectroscopy of a high density, high temperature aluminum plasma. Extensions to other types of spectroscopy data analysis are discussed.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1063/1.1143558
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