How to use neural network and web technologies in modeling complex technical systems
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URI (для ссылок/цитирований):
https://iopscience.iop.org/article/10.1088/1757-899X/537/3/032095https://elib.sfu-kras.ru/handle/2311/129222
Автор:
Semenenko, M. G.
Kniazeva I.V
Beckel, L. S.
Rutskiy, V. N.
Tsarev, R. Y.
Yamskikh, T. N.
Kartsan, I. N.
Коллективный автор:
Институт космических и информационных технологий
Институт экономики, управления и природопользования
Кафедра разговорного иностранного языка
Кафедра экономических теорий
Кафедра информатики
Дата:
2019-06Журнал:
IOP Conference Series: Materials Science and EngineeringКвартиль журнала в Scopus:
без квартиляКвартиль журнала в Web of Science:
без квартиляБиблиографическое описание:
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).Аннотация:
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.