Predicting the Functional Dependence of the Sunspot Number in the Solar Activity Cycle Based on Elman Artificial Neural Network

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

The possibility of predicting the function of the time dependence of the sunspot number (SSN) in
the solar activity cycle is analyzed based on the application of the Elman artificial neural network platform to
the historical series of observational data. A method for normalizing the initial data for preliminary training
of the ANN algorithm is proposed, in which a sequence of virtual idealized cycles is constructed using scaled
duration coefficients and the amplitude of solar cycles. The correctness of the method is analyzed in a numerical
experiment based on modeling the time series of sunspots. The intervals of changing the adaptable
parameters in the ANN operation are estimated and a mathematical criterion for choosing a solution is proposed.
The significant asymmetry of its ascending and descending branches is a characteristic property of the
constructed functional dependence of the sunspot number cycle. A forecast of the time course for the current
25th cycle of solar activity is presented and its correctness is discussed in comparison with other forecast
results and the available data of solar activity status monitoring

Авторлар туралы

I. Krasheninnikov

Pushkov Institute of Terrestrial Magnetism, the Ionosphere, and Radio Wave Propagation, Russian Academy of Sciences
(IZMIRAN)

Email: krash@izmiran.ru
Moscow, Troitsk, 108840 Russia

S. Chumakov

Pushkov Institute of Terrestrial Magnetism, the Ionosphere, and Radio Wave Propagation, Russian Academy of Sciences
(IZMIRAN)

Хат алмасуға жауапты Автор.
Email: krash@izmiran.ru
Moscow, Troitsk, 108840 Russia

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© И.В. Крашенинников, С.О. Чумаков, 2023