Application of artificial neural networks in studying the dynamic structure of the near-Earth orbital space
A description of the technique for studying the dynamic structure of the near-Earth orbital space using machine learning technology is presented. Artificial neural networks were used to process time series associated with the evolution of resonance characteristics that determine the dynamic structure of the near-Earth region up to 120 thousand km along the semi-major axis. The number of the processed series has exceeded half a million, and their manual processing would be time consuming. The results of applying the technique to the analysis of the resonant structure of the selected area of space are presented.
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Keywords
numerical modeling, AES dynamics, orbital evolution, artificial neural networks, machine learningAuthors
| Name | Organization | |
| Krasavin D.S. | National Research Tomsk State University | iosfixed@gmail.com |
| Aleksandrova A.G. | National Research Tomsk State University | aleksandrovaannag@mail.ru |
| Tomilova I.V. | National Research Tomsk State University | irisha_tom@mail.ru |
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