Abstract
Space debris (SD) is a real danger for spacecraft (S) in-orbit operation. Taking this danger into account is a S flight safety requirement. SD particles are detected by the S on-board equipment. The integrated intelligent information system forecasts, within its execution time, the results of the impact caused by these particles. Such forecasting enables one to evaluate potential damage from the collision and to take sufficient measures to ensure the S safety. The article presents an approach to forecasting the results of dynamic interaction between SD objects and a S on the basis of fuzzy logic rules and the mechanism of knowledge base training, carried out by generative adversarial network (GAN).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zade, L.A.: The role of soft computing and fuzzy logic in understanding, designing and developing information/intelligent systems. Fizmat, Moscow (2001)
Mironov, V.V., Tolkach, M.A.: The ballistic limit equations for optimization of system of protection of spacecrafts against micrometeoroid and space debris. Space Tech. Technol. 3(14), 26 (2016)
Mityushkin, Yu.I., Mokin B.I., Rotshteyn A.P.: Soft computing: identification of regularities indistinct knowledge bases. Universum, Vinnitsa (2002)
GAN- generative adversarial network. http://robocraft.ru/blog/machinelearning/3693.html. Accessed 13 May 2018
Zimmermann, H.J.: Fuzzy Set Theory - and Its Applications. Kluwer, Dordrecht (1991)
Gorban, A.N., Rossiev, D.A.: Neural networks on a personal computer. Nauka, Novosibirsk (1996)
OST 134-1031-2003: Space technique products. General requirements to aerospace systems security against mechanical impacts caused by particles of natural and anthropogenic origin
Li, Y., Jiang, Y., Huang, C.: Shape design of lifting body based on genetic algorithm. Int. J. Intell. Syst. Appl. (IJISA) 1, 37–43 (2010)
Bodyanskiy, Y.V., Tyshchenko, O.K., Kopaliani, D.S.: An extended neo-fuzzy neuron and its adaptive learning algorithm. Int. J. Intell. Syst. Appl. 02, 21–26 (2015)
Rotshteyn, A.P.: Intellectual technologies of identification: fuzzy logic, genetic algorithms, neural networks. Universum, Vinnitsa (1999)
Acknowledgements
The research was done within the government task of the Ministry of Education and Science of the Russian Federation. The number for the publication is 2.1777.2017/4.6.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Paliukh, B.V., Kemaykin, V.K., Kozlova, Y.G., Kozhukhin, I.V. (2019). Forecasting of Results of Dynamic Interaction Between Space Debris and Spacecrafts on the Basis of Soft Computing Methods. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-01818-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01817-7
Online ISBN: 978-3-030-01818-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)