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Please use this identifier to cite or link to this item: http://hdl.handle.net/2014/42066

Title: Identification of abnormal system noise temperature patterns in Deep Space Network antennas using neural network trained fuzzy logic
Authors: Lu, Thomas
Pham, Timothy
Liao, Jason
Keywords: Deep Space Network (DSN)
neural network training
fuzzy logic
pattern identification
system noise temperature
Issue Date: 17-Apr-2011
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011.
Citation: The 3rd International Conference on Advances in Satellite and Space Communication, Budapest, Hungary, April 17-22, 2011
Abstract: This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected noncorrelations which merit further study in the future.
URI: http://hdl.handle.net/2014/42066
Appears in Collections:JPL TRS 1992+

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