Home // SPACOMM 2011, The Third International Conference on Advances in Satellite and Space Communications // View article
Authors:
Thomas Lu
Timothy Pham
Jason Liao
Keywords: Deep Space Network; Neural network training; Fuzzy logic; Pattern identification; System noise temperature; Link margin.
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.
Pages: 35 to 40
Copyright: Copyright (c) IARIA, 2011
Publication date: April 17, 2011
Published in: conference
ISSN: 2308-4480
ISBN: 978-1-61208-128-1
Location: Budapest, Hungary
Dates: from April 17, 2011 to April 22, 2011