Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model
Year: 2024
Type: article
Abstract: In this research report, the prediction method of sunspot activity is deeply discussed, and a variety of statistical and data science models are used to make comprehensive prediction. Research focuses include the start time and duration of the solar maximum in the solar cycle, as well as the prediction of the number and area of sunspots. By capturing the periodicity of solar activity, Pearson correlation coefficient is used to analyze the relationship between the maximum solar activity and the number of sunspots, and the adaptive multivariate nonlinear regression-BP neural network model and particle swarm optimization BP neural network are combined to make high-precision prediction. The research results provide a scientific basis for the prediction of space weather, ionospheric state and communication system reliability.
DOI: 10.54097/562ea683
Authors: Ziyang Yu, Xinpeng Yu, Yinuo Liu, Yue Li, Yuyan Zeng, Yanqing Liu
Institution: Guilin Institute of Information Technology
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404767262