Neural network prediction of solar cycle 24

• Published in: Research in astronomy and astrophysics Vol. 11; no. 4; pp. 491 - 496
• Main Authors: Ajabshirizadeh, A, Jouzdani, N. Masoumzadeh, Abbassi, Shahram
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.04.2011; Department of Physics, University of Tabriz, Tabriz, Iran%Department of Physics, University of Tabriz, Tabriz, Iran; School of Astronomy, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran%School of Physics, Damghan University, E O. Box 36175-364 Damghan, Iran; School of Astronomy, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
• Subjects: 地球环境; 太阳周期; 太阳活动周期; 太阳活动预报; 太阳黑子数; 神经网络技术; 神经网络方法; 神经网络预测; Sun; activity - sunspots - neural networks - prediction
• ISSN: 1674-4527; 2397-6209
• DOI: 10.1088/1674-4527/11/4/011

The ability to predict the future behavior of solar activity has become extremely import due to its effect on the environment near the Earth. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of space weather. The level of solar activity is usually expressed by in- ternational sunspot number （Rz）. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. We predict a solar index （Rz） in solar cycle 24 by using a neural network method. The neural network technique is used to analyze the time series of solar activity. According to our predictions of yearly sunspot number, the maximum of cycle 24 will occur in the year 2013 and will have an annual mean sunspot number of 65. Finally, we discuss our results in order to compare them with other suggested predictions.