计算机工程2011，Vol.37Issue(24)：176-179,4.DOI:10.3969/j.issn.1000-3428.2011.24.059
基于EMD的太阳黑子时间序列组合预测模型
Composite Forecasting Model of Sunspot Time Sequences Based on EMD
摘要
Abstract
According to the complexity of sunspots, this paper uses Empirical Mode Decomposition(EMD) method, the solar activity contains all of the time scale changes separated into the inherent weight smooth Intrinsic Mode Function(IMF) and remainders. It observes each component of the spectrum, based on the characteristics of the low frequency IMF component selection Auto-regressive Moving Average(ARMA) model predicted the average directly, and the high frequency IMF using neural network forecast. Through the various components of the primary signal reconstruction predicts a prediction sequence, and increases the prediction accuracy. Simulation results show that the model has higher forecast accuracy.关键词
太阳黑子数/经验模态分解方法/自回归滑动平均模型/反向传播Key words
sunspot number/ Empirical Mode Decomposition(EMD) method/ Auto-regressive Moving Average(ARMA) model/ Back Propagation(BP)分类
信息技术与安全科学引用本文复制引用
王曦,毕贵红,唐京瑞..基于EMD的太阳黑子时间序列组合预测模型[J].计算机工程,2011,37(24):176-179,4.基金项目
云南省自然科学基金资助项目(2009CD028) （2009CD028）
昆明理工大学科学研究基金资助项目(201001) （201001）
