Труды КНЦ (Естественные и гуманитарные науки вып.3/2025(4))
5. Kan J. R., Lee L. C. Energy coupling function and solar wind-magnetosphere dynamo // Geophys. Res. Lett. 1979. Vol. 6. Р. 577-580. 6. A nearly universal solar wind-magnetosphere coupling function inferred from 10 magnetospheric state variables / P. T. Newel [et al.] // J. Geophys. Res. 2007. Vol. 112. P. A01206. doi:10.1029/2006JA012015. 7. Borovsky J. E., Birn J. The solar wind electric field does not control the dayside reconnection rate // J. Geophys. Res. Space Physics. 2014. Vol. 119. P. 753-760. doi:10.1002/2013JA019193. 8. Borovsky J. E. Canonical correlation analysis of the combined solar-wind and geomagnetic-index data sets // J. Geophys. Res. 2014. Vol. 119. P. 5364-5381. doi:10.1002/2013JA019607. 9. Бархатов Н. А., Ревунов С. Е. Искусственные нейронные сети в задачах солнечно-земной физики. Н. Новгород: Поволжье, 2010. 407 с. 10. Neural network classification of substorm geomagnetic activity caused by solar wind magnetic clouds / N. A. Barkhatov [et al.] // J. Atmosph. and Solar-Terr. Phys. 2020. Vol. 205. Р. 105301. 11. Hochreiter S., Schmidhuber J. Long short-term memory // Neural computation. 1997. Vol. 9 (8). Р. 1735-1780. 12. Козелов Б. В. Предсказание временных рядов солнечной активности с помощью искусственной нейронной сети LSTM // Труды Кольского НЦ РАН. Серия Естественные и гуманитарные науки. 2023. Т. 2, № 2. С. 19 24. doi:10.37614/2949-1185.2023.2.2.003. 13. Multiple-hour ahead forecast of the Dst index using a combination of long short-term memory neural network and Gaussian process / M. A. Gruet [et al.] // Space Weather. 2018. Vol. 16. Р. 1882-1896. https://doi.org/10.1029/2018SW001898. 14. Geomagnetic index Kp forecasting with LSTM / Y. Tan [et al.] // Space Weather. 2018. Vol. 1. Р. 406-416. https://doi.org/10.1002/2017SW001764. References 1. Shubin V. N., Deminov M. G. Global'naya dinamicheskaya model' kriticheskoj chastoty F 2 -sloya ionosfery [Global dynamic model of the critical frequency of the F 2 layer of the ionosphere]. Geomagnetizm i aeronomiya [Geomagnetism and Aeronomy], 2019, Vol. 59, no. 4, pp. 461-473. (In Russ.). 2. Vorobjev V. G., Yagodkina O. I., Katkalov Y. Auroral Precipitation Model and its applications to ionospheric and magnetospheric studies. J. Atm. S-Terr. Phys., 2013, Vol. 102 (9), pp. 157-171. 3. Burton R. K., McPherron R. L., Russel C. J. An empirical relationship between interplanetary conditions and Dst. Geophys. Res., 1975, Vol. 80, pp. 4204-4214. 4. Dremuhina L. A., Lodkina I. G., Ermolaev Yu. Svyaz' parametrov solnechnogo vetra raznyh tipov c indeksami geomagnitnoj aktivnosti [Relationship of solar wind parameters of different types with geomagnetic activity indices]. Kosmicheskie issledovaniya [Space Research], 2018, Vol. 56, no. 6, pp. 410-419. (In Russ.). 5. Kan J. R., Lee L. C. Energy coupling function and solar wind-magnetosphere dynamo. Geophys. Res. Lett., 1979, Vol. 6, pp. 577-580. 6. Newel P. T., Sotirelis T., Liou K., Meng C. I., Rich F. A nearly universal solar wind-magnetosphere coupling function inferred from 10 magnetospheric state variables. J. Geophys. Res., 2007, Vol. 112, pp. A01206. doi:10.1029/2006JA012015. 7. Borovsky J. E., Birn J. The solar wind electric field does not control the dayside reconnection rate. J. Geophys. Res. Space Physics, 2014, Vol. 119, pp. 753-760. doi:10.1002/2013JA019193. 8. Borovsky J. E. Canonical correlation analysis of the combined solar-wind and geomagnetic-index data sets. J. Geophys. Res., 2014, Vol. 119, pp. 5364-5381. doi:10.1002/2013JA019607. 9. Barkhatov N. A., Revunov S. E. Iskusstvennye nejronnye seti v zadachah solnechno-zemnoj fiziki [Artificial neural networks in solar-terrestrial physics problems]. N. Novgorod, Povolzh'e, 2010, 407 p. 10. Barkhatov N. A., Vorobjev V. G., Revunov S. E., Barkhatova O. M., Revunova E. A., Yagodkina O. I. Neural network classification of substorm geomagnetic activity caused by solar wind magnetic clouds. J. Atmospheric and Solar-Terrestrial Physics, 2020, Vol. 205 (2), pp. 105301. 11. Hochreiter S., Schmidhuber J. Long short-term memory. Neural computation, 1997, Vol. 9 (8), pp. 1735-1780. 12. Kozelov B. V. Predskazanie vremennyh ryadov solnechnoj aktivnosti s pomoshch'yu iskusstvennoj nejronnoj seti LSTM [Prediction of solar activity time series using LSTM artificial neural network]. Trudy Kol'skogo nauchnogo centra RAN [Transactions of the Kola Science Centre of RAS. Series: Natural Sciences and Humanities], 2023, Vol. 2, no. 2, рp. 19-24. doi:10.37614/2949-1185.2023.2.2.003 (In Russ.). Труды Кольского научного центра РАН. Серия: Естественные и гуманитарные науки. 2025. Т. 4, № 3. С. 56-65. Transactions of the Kola Science Centre of RAS. Series: Natural Sciences and Humanities. 2025. Vol. 4, No. 3. P. 56-65. © Козелов Б. В., 2025 64
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