【摘要】 地球变化磁场的建模与预测是地磁导航和空间环境监测领域的重要研究课题。本文提出了一种新的基于回声状态网络（Echo State Network, ESN）、集成经验模态分解（Ensemble Empirical Mode Decomposition, EEMD）和复杂性理论样本熵（Sample entropy, SampEn）的组合预测模型。首先，使用EEMD-SampEn将地磁变化时间序列分解为多个复杂度不同的地磁变化子序列。接着，为每个子序列构建基于ESN的预测模型，选择最优的模型参数。最后，利用地磁台站收集的实际数据进行模拟验证。结果表明，该组合模型的预测值能够紧密贴合地球变化磁场的趋势，且明显优于最小二乘支持向量机模型，体现出更好的预测效果，当Kp指数低于3时，预测3h的平均绝对误差小于1.40nT。更多还原

【Abstract】 Modeling and forecasting of the geomagnetic variation are important research topics concerning geomagnetic navigation and space environment monitoring.We propose a combined forecasting model using a dynamic recursive neural network called echo state network(ESN),the method of complementary ensemble empirical mode decomposition(EEMD) and the complexity theory of sample entropy(SampEn).Firstly,we use EEMD-SampEn to decompose the geomagnetic variation time series into many series of geomagnetic variation subsequences whose complexity degrees are transparently different.Then,we use ESN to build a forecasting model for each subsequence,selecting the optimal model parameters.Finally,we use the real data collected from the geomagnetic observatory to conduct simulations.The results show that the forecasting value of the combined model can closely conform to the tendency of geomagnetic variation field,and is superior to the least square support vector machine(LSSVM) model.The mean absolute error of the model for three-hour forecasting is less than 1.40nT when Kp index is less than 3.更多还原