摘要
在电离层风暴期,现存的电离层F2层临界频率预测方法不能满足实际应用的要求.根据磁层ap系数和太阳黑子月均值作为风暴期训练序列,提出了一种基于神经网络的电离层F2层临界频率预测新方法.模拟结果表明,这种新方法比现有的预测方法(STORM模型和Cander提出的神经网络方法)具有更好的预测性能.
During ionospheric storm periods, the existing prediction methods for the critical frequency of ionospheric F2 layer （foF2） can not satisfy the requirements of the practical applications. In this paper, a new prediction method of the foF2 based on neural networks is proposed. This method is driven by the previous time series of the ap index and the monthly mean of the solar spot number, while the output is the estimations of the foF2 in the next 24 hours. The simulation results indicate that the new method outperforms the existing prediction methods （such as STORM model, or neural network method proposed by Cander）.
出处
《中国科学院研究生院学报》
CAS
CSCD
2008年第3期403-407,共5页
Journal of the Graduate School of the Chinese Academy of Sciences
基金
“863”国家科技计划(2007AA01Z297)
国家“十一五”项目资助
关键词
FOF2
神经网络
风暴期
电离层
太阳活动
foF2, neural networks, storm periods, ionosphere, solar activity
