發(fā)布時間：2019-03-07 11:12

【摘要】：太陽黑子月均值是典型的混沌時間序列,具有較強的非線性和非平穩(wěn)特征,能夠反映太陽活動的真實水平。采用一種應(yīng)用集合經(jīng)驗?zāi)B(tài)分解(Ensemble Empirical Mode Decomposition,EEMD)與徑向基函數(shù)(Radial Basis Function,RBF)神經(jīng)網(wǎng)絡(luò)組合的預(yù)測模型。通過EEMD將原始時間序列分解為若干個不同時間尺度的本征模態(tài)函數(shù)(Intrinsic Mode Function,IMF)分量,并對這些分量進(jìn)行建模預(yù)測,再將各分量的預(yù)測值重構(gòu)得到原始時間序列的預(yù)測值,這樣不僅降低了算法的復(fù)雜性,而且有利于提高模態(tài)分量包含信息的物理意義。仿真結(jié)果表明,與經(jīng)驗?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)結(jié)合RBF神經(jīng)網(wǎng)絡(luò)的模型相比,該模型具有較高的預(yù)測精度。
[Abstract]:The monthly mean of sunspot is a typical chaotic time series with strong nonlinear and non-stationary characteristics and can reflect the true level of solar activity. A prediction model based on the combination of set empirical mode decomposition (Ensemble Empirical Mode Decomposition,EEMD) and radial basis function (Radial Basis Function,RBF) neural networks is proposed. The original time series is decomposed into several intrinsic modal function (Intrinsic Mode Function,IMF) components with different time scales by EEMD, and these components are modeled and predicted, then the predicted values of each component are reconstructed to get the predicted values of the original time series. This not only reduces the complexity of the algorithm, but also improves the physical meaning of modal component inclusion information. The simulation results show that compared with the empirical mode decomposition (Empirical Mode Decomposition,EMD) model combined with the RBF neural network model, the proposed model has a higher prediction accuracy than that of the empirical mode decomposition (EMD) model.
【作者單位】： 西安郵電大學(xué)電子工程學(xué)院;
【基金】：陜西省自然科學(xué)基金(No.2014JM8331)
【分類號】：P182.41;TP183
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本文編號：2436064