Date of Award
Master of Science in Engineering
Mechanical and Aerospace Engineering
Dr. Jennifer Hudson
Dr. Richard Meyer
Dr. Damon Miller
artificial neural networks, state trajectories, machine learning, dynamic systems, training set data
Masters Thesis-Open Access
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.
Reinke, "Training Set Density Estimation for Trajectory Predictions Using Artificial Neural Networks" (2019). Master's Theses. 4302.