计算机技术与发展2016，Vol.26Issue(3)：158-161,4.DOI:10.3969/j.issn.1673-629X.2016.03.037
太阳黑子活动周期特征的神经网络和小波分析
BP Neural Network and Wavelet Analysis of Period of Sunspot Activity
摘要
Abstract
The sunspot number is the main indicator of the level of solar activity,solar activity directly affects the daily environment. Based on the sunspot number observation data of the predecessor,using BP neural network and wavelet analysis and self integrating meth-od,the 1770-1869 sunspot number mean is analyzed,it is concluded that the sunspots are 11-12 year cycle,and the algorithm and its noise robustness is simulated. The experimental results show that the algorithm is effective for the essential rule of solar activity. Two methods with other methods,such as self correlation method,the power spectrum method,are compared to not only draw the practical conclusions but also have the strong robustness for noise,which is very significant for noise signal analysis.关键词
太阳黑子数/BP神经网络/小波分析/自相关/周期/鲁棒性Key words
sunspot numbers/BP neural network/wavelet analysis/autocorrelation/cycle/robustness分类
信息技术与安全科学引用本文复制引用
潘春花,孙燕,朱存..太阳黑子活动周期特征的神经网络和小波分析[J].计算机技术与发展,2016,26(3):158-161,4.基金项目
青海省自然科学基金(2013-Z-920) （2013-Z-920）
