TY - GEN
T1 - Forecast of monthly sunspot numbers using nonlinear dynamo model with neural networks
AU - Safiullin, N.
AU - Porshnev, S.
AU - Kleeorin, N.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/22
Y1 - 2017/12/22
N2 - 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%.
AB - 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%.
KW - artificial neural networks
KW - data analysis
KW - solar cycle simulation
KW - sunspot numbers
KW - time series forecast
UR - http://www.scopus.com/inward/record.url?scp=85045977786&partnerID=8YFLogxK
U2 - 10.1109/Dynamics.2017.8239500
DO - 10.1109/Dynamics.2017.8239500
M3 - Conference contribution
AN - SCOPUS:85045977786
T3 - 11th International IEEE Scientific and Technical Conference "Dynamics of Systems, Mechanisms and Machines", Dynamics 2017 - Proceedings
SP - 1
EP - 4
BT - 11th International IEEE Scientific and Technical Conference "Dynamics of Systems, Mechanisms and Machines", Dynamics 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 11th International IEEE Scientific and Technical Conference "Dynamics of Systems, Mechanisms and Machines", Dynamics 2017
Y2 - 14 November 2017 through 16 November 2017
ER -