Author

Reinke

Date of Award

4-2019

Degree Name

Master of Science in Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Dr. Jennifer Hudson

Second Advisor

Dr. Richard Meyer

Third Advisor

Dr. Damon Miller

Keywords

artificial neural networks, state trajectories, machine learning, dynamic systems, training set data

Access Setting

Masters Thesis-Open Access

Abstract

Demand on earth orbiting surveillance systems in increasing as more equipment is put into orbit. These systems rely on predictive techniques to periodically track objects. The demand on these systems may be reduced if object trajectory data to develop scalable training sets used for training artificial neural networks (ANNs) to predict trajectories of a dynamic system. These methods use multi-variable statistics to analyze data energy content to provide the ANN with low density, feature-rich, training data. The developed techniques have been shown to increase ANN prediction accuracy while reducing the size of the training set when applied to a linear dynamic system. These methods may find future application in predicting satellite trajectories.

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