This paper illustrates the applicability of neural network in St classifying events using Space Acceleration Measurement PI 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 back propagation 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, A., Sinha, A., Grodsinsky, C.M., Neural-Network-Based Classification of Space Acceleration Measurement Systems (SAMS) Data Via Supervised Learning, International Journal for Microgravity Research and Applications, Microgravity Science and Technology, Hanser Publisher, Munich, Germany, Vol. IX/2, pp.117-124, 1996 .