Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Sergey Dolenko"'
Autor:
Igor Isaev, Ivan Obornev, Eugeny Obornev, Eugeny Rodionov, Mikhail Shimelevich, Sergey Dolenko
Publikováno v:
Procedia Computer Science. 213:777-784
Autor:
Igor Isaev, Ivan Obornev, Eugeny Obornev, Eugeny Rodionov, Mikhail Shimelevich, Sergey Dolenko
Publikováno v:
Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022).
Autor:
Nickolay Shchurov, Igor Isaev, Sergey Burikov, Tatiana Dolenko, Kirill Laptinskiy, Sergey Dolenko
Publikováno v:
Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022).
Publikováno v:
Studies in Computational Intelligence ISBN: 9783031190315
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07a2354077967cae52b6b5cd8955c008
https://doi.org/10.1007/978-3-031-19032-2_16
https://doi.org/10.1007/978-3-031-19032-2_16
Publikováno v:
Studies in Computational Intelligence ISBN: 9783031190315
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::119ecd5071270a394cb60220bddd40d5
https://doi.org/10.1007/978-3-031-19032-2_44
https://doi.org/10.1007/978-3-031-19032-2_44
Autor:
Igor Isaev, Ivan Obornev, Eugeny Obornev, Eugeny Rodionov, Mikhail Shimelevich, Sergey Dolenko
Publikováno v:
2022 VIII International Conference on Information Technology and Nanotechnology (ITNT).
Autor:
Igor Isaev, Ismail Gadzhiev, Olga Sarmanova, Sergey Burikov, Tatiana Dolenko, Kirill Laptinskiy, Sergey Dolenko
Publikováno v:
Laser Physics, Photonic Technologies, and Molecular Modeling.
Autor:
Irina Myagkova, O. G. Barinov, Yu. S. Shugai, Vladimir Shirokii, R. D. Vladimirov, Sergey Dolenko
Publikováno v:
Russian Meteorology and Hydrology. 46:163-171
The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respect
Publikováno v:
Russian Meteorology and Hydrology. 46:157-162
The potential is investigated of predicting the time series of the Dst geomagnetic index using various adaptive methods: artificial neural networks (classical multilayer perceptrons), decision trees (random forest), gradient boosting. The prediction
Publikováno v:
Geomagnetism and Aeronomy. 61:138-147
The possible use of artificial neural networks—classical multilayer perceptrons—with coupling functions to forecast time series of the Dst geomagnetic index is studied. The basic forecast is based on parameters of the solar wind and interplanetar