Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model

Ziyang Yu; Xinpeng Yu; Yinuo Liu; Yue Li; Yuyan Zeng; Yanqing Liu

doi:10.54097/562ea683

Authors

Ziyang Yu
Xinpeng Yu
Yinuo Liu
Yue Li
Yuyan Zeng
Yanqing Liu

DOI:

https://doi.org/10.54097/562ea683

Keywords:

Neural Network, Pearson Correlation Coefficient, Differential Evolution Algorithm, Particle Swarm Optimization

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.

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