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
Published Version
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