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