本文選題：太陽黑子　切入點：EMD分解　出處：《鄭州大學(xué)學(xué)報(工學(xué)版)》2014年03期

【摘要】：太陽黑子是非線性、非平穩(wěn)、多尺度變化的時間序列,且觀測結(jié)果大多存在噪聲的干擾.針對太陽黑子時間序列預(yù)測的復(fù)雜性,首先將原始數(shù)據(jù)序列通過小波去噪進行預(yù)處理,然后將去噪后的信號通過EMD分解產(chǎn)生若干個從高頻到低頻的IMF分量和余項.針對低頻分量變化緩慢和高頻分量波動性較大的特點,分別采用RBF神經(jīng)網(wǎng)絡(luò)模型和SVM模型進行預(yù)測,最后將各個分量的預(yù)測結(jié)果相疊加得到最終預(yù)測值.仿真結(jié)果表明,該模型具有較高的預(yù)測精度.
[Abstract]:Sunspots are nonlinear, non-stationary and multi-scale time series, and most of the observed results are disturbed by noise.In view of the complexity of sunspot time series prediction, the original data sequence is preprocessed by wavelet denoising, and then the de-noised signal is decomposed by EMD to produce several IMF components and remainder from high frequency to low frequency.In view of the slow change of low-frequency components and the large volatility of high-frequency components, the RBF neural network model and the SVM model are used to predict, respectively. Finally, the final prediction values are obtained by superposing the prediction results of each component.The simulation results show that the model has high prediction accuracy.
【作者單位】： 鄭州大學(xué)電氣工程學(xué)院;
【基金】：河南省科技攻關(guān)計劃項目(122102210102)
【分類號】：P182.41

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