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Semenenko, M. G.
Kniazeva I.V
Beckel, L. S.
Rutskiy, V. N.
Tsarev, R. Y.
Yamskikh, T. N.
Kartsan, I. N.
2020-01-20T07:56:14Z
2020-01-20T07:56:14Z
2019-06
Semenenko, M. G. How to use neural network and web technologies in modeling complex technical systems [Текст] / M. G. Semenenko, Kniazeva I.V, L. S. Beckel, V. N. Rutskiy, R. Y. Tsarev, T. N. Yamskikh, I. N. Kartsan // IOP Conference Series: Materials Science and Engineering. — 2019. — Т. 537 (№ 3).
https://iopscience.iop.org/article/10.1088/1757-899X/537/3/032095
http://elib.sfu-kras.ru/handle/2311/129222
This paper discusses the problem of integrating modern methods of forecasting and modeling complex technical objects into the learning process. As an example, the problem of solving a system of ordinary differential equations is considered, which has significant practical application. In particular, solving a system of differential equations can be an essential part of patents. The neural network method to solve this problem by using Matlab simulation software and visual modeling tool Simulink is considered. Efficient cloud-based solution to ordinary differential equations is presented.
Learning process
Matlab simulations
Model complexes
Neural network method
System of differential equations
How to use neural network and web technologies in modeling complex technical systems
Journal Article
Journal Article Postprint
14.85.09
2020-01-20T07:56:14Z
Институт космических и информационных технологий
Институт экономики, управления и природопользования
Кафедра разговорного иностранного языка
Кафедра экономических теорий
Кафедра информатики
IOP Conference Series: Materials Science and Engineering
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без квартиля


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