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THE USE OF MACHINE LEARNING TO ADAPT THE STRUCTURE OF MILITARY COMPUTING SYSTEMS IN UAV CONTROL TASKS

Vorganov Alexander Alexandrovich  (Head of the educational unit – Deputy Head of the Department of VKS RTU MIREA )

Kurdyumov Ivan Alekseevich  (RTU MIREA)

Prokhorov Vadim Alexandrovich  (RTU MIREA)

Zhizhin Gleb Vladimirovich  (RTU MIREA)

This article discusses the application of machine learning to adapt the structure of military computing systems in the tasks of controlling unmanned aerial vehicles (UAVs). The rapid development of military technologies requires increasing flexibility and adaptability from computing systems, which makes traditional UAV control methods insufficiently effective. Machine learning offers solutions that can provide the high accuracy, responsiveness and adaptability that are critical to the successful execution of military operations. The implementation of such systems requires careful planning and consideration of many factors, but the results justify the efforts, providing significant improvements in the efficiency and reliability of UAV control.

Keywords:Machine learning, unmanned aerial vehicles (UAVs), military computing systems, adaptability, UAV control.
 

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Citation link:
Vorganov A. A., Kurdyumov I. A., Prokhorov V. A., Zhizhin G. V. THE USE OF MACHINE LEARNING TO ADAPT THE STRUCTURE OF MILITARY COMPUTING SYSTEMS IN UAV CONTROL TASKS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2025. -№04. -С. 35-40 DOI 10.37882/2223-2966.2025.04.03
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