@inproceedings{7d84df574ef54df397736764e99574e1,
title = "Forecast of monthly sunspot numbers using nonlinear dynamo model with neural networks",
abstract = "In this paper we overview the modern forecast methods of monthly sunspot numbers, such as McNish-Lincoln and Hathaway-Wilson-Reichmann standard curve-fitting. Their disadvantages are presented, leading us to the necessity of researching a new technique for the solar activity prediction. For the long-term forecast we propose to use the established nonlinear dynamo model based on negative effective magnetic pressure instability. For the short-term forecast adjustment we propose to use instead of Data Assimilation technique the Artificial Neural Networks. The implemented NAR- and NARX-nets have been trained on the 3 sunspot cycles (between 1965 and 1997), with the testing forecast into the next solar cycle up to 2009 year. The final results show plausible forecast accuracy: the misfit coefficient is only between 20-35\%.",
keywords = "artificial neural networks, data analysis, solar cycle simulation, sunspot numbers, time series forecast",
author = "N. Safiullin and S. Porshnev and N. Kleeorin",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 11th International IEEE Scientific and Technical Conference {"}Dynamics of Systems, Mechanisms and Machines{"}, Dynamics 2017 ; Conference date: 14-11-2017 Through 16-11-2017",
year = "2017",
month = dec,
day = "22",
doi = "10.1109/Dynamics.2017.8239500",
language = "English",
series = "11th International IEEE Scientific and Technical Conference \"Dynamics of Systems, Mechanisms and Machines\", Dynamics 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1--4",
booktitle = "11th International IEEE Scientific and Technical Conference {"}Dynamics of Systems, Mechanisms and Machines{"}, Dynamics 2017 - Proceedings",
address = "United States",
}