Neural-network-based classification of Space Acceleration Measurement Systems (SAMS) data via supervised learning.
This paper illustrates the applicability of neural networks in classifying events using Space Acceleration Measurement System (SAMS) data.  Computer programs have been written in the MATLAB environment for the following purposes:  automatic retrieval of SAMS data from NASA CDROM disks, computation of power spectral densities for SAMS data and construction of input patterns for the training of a multi-layer neural network (MNN).  The MNN has been trained using the backpropagation learning algorithm and the SAMS data collected on the STS-50 Space Shuttle mission for three crew exercise events.  It is found that the trained MNN is highly successful in classifying events.  In addition, the performance of MNN is found to be better than that of the nearest neighbor classifier.
Smith
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Department of Mechanical Engineering, The Pennsylvania State University, University Park 16802, USA.
Sinha
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Grodsinsky
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D000054:Acceleration; D000465:Algorithms; D016251:CD-ROM; D016208:Databases, Factual; D015444:Exercise; D006801:Humans; D016571:Neural Networks, Computer; D012984:Software; D013026:Space Flight / Q000295:instrumentation*; D018531:Spacecraft / Q000295:instrumentation*; D014893:Weightlessness