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High Reliability Neural Networks Structure with Application to Spacecraft ASMS Tone Kourosh Rahnamai John Maleyeff Payman Arabshahi, Tsun-Yee Yan
 

Summary: High Reliability Neural Networks Structure with Application to Spacecraft ASMS Tone
Detection
Kourosh Rahnamai John Maleyeff Payman Arabshahi, Tsun-Yee Yan
Western New England College Rensselaer Polytechnic Institute Jet Propoulsion Laboratory/Caltech
Electrical Engineering Department Lally School of Mangement Digital Signal Processing Research Group
1215 Wilbraham Road 275 Windsor St., 4800 Oak grove drive, MS 238-343
Spring field, MA 01119 USA Hartford, CT 06120 Pasadena, CA 91109 USA
krahnama@wnec.edu Maleyeff@rh.edu [payman,yah]@dsp.jpl.nasa.gov
Abstract
In this study we will show that the research on N-
version high-reliability software structures can be
extended to neural networks architecture. In addition,
we will explore the possibility of applying this
structure to a spacecraft tracking problem. One such
system is the Automated Spacecraft Monitoring
System (ASMS), a beacon-monitoring or detection
system. Four neural networks, each trained for various
operating environments, are implemented in an N-
version structure. The results of the networks are
combined to form a composite outcome. The

  

Source: Arabshahi, Payman - Applied Physics Laboratory & Department of Electrical Engineering, University of Washington at Seattle

 

Collections: Engineering