Predicting the Functional Dependence of the Sunspot Number in the Solar Activity Cycle Based on Elman Artificial Neural Network-Reference-Cited by-同舟云学术

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

Published:2023-03-01 Issue:2 Volume:63 Page:247-256
ISSN:0016-7940
Container-title:Геомагнетизм и аэрономия
language:
Short-container-title:Geomagnetizm i aèronomiâ

Author:

Krasheninnikov I. V.¹,Chumakov S. O.¹

Affiliation:

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

Abstract

The possibility of predicting the function of the time dependence of the sunspot number (SSN) inthe solar activity cycle is analyzed based on the application of the Elman artificial neural network platform tothe historical series of observational data. A method for normalizing the initial data for preliminary trainingof the ANN algorithm is proposed, in which a sequence of virtual idealized cycles is constructed using scaledduration coefficients and the amplitude of solar cycles. The correctness of the method is analyzed in a numericalexperiment based on modeling the time series of sunspots. The intervals of changing the adaptableparameters 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 theconstructed functional dependence of the sunspot number cycle. A forecast of the time course for the current25th cycle of solar activity is presented and its correctness is discussed in comparison with other forecastresults and the available data of solar activity status monitoring

Publisher

The Russian Academy of Sciences

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