發(fā)布時(shí)間：2018-04-05 13:30

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

【摘要】：太陽(yáng)黑子是非線性、非平穩(wěn)、多尺度變化的時(shí)間序列,且觀測(cè)結(jié)果大多存在噪聲的干擾.針對(duì)太陽(yáng)黑子時(shí)間序列預(yù)測(cè)的復(fù)雜性,首先將原始數(shù)據(jù)序列通過(guò)小波去噪進(jìn)行預(yù)處理,然后將去噪后的信號(hào)通過(guò)EMD分解產(chǎn)生若干個(gè)從高頻到低頻的IMF分量和余項(xiàng).針對(duì)低頻分量變化緩慢和高頻分量波動(dòng)性較大的特點(diǎn),分別采用RBF神經(jīng)網(wǎng)絡(luò)模型和SVM模型進(jìn)行預(yù)測(cè),最后將各個(gè)分量的預(yù)測(cè)結(jié)果相疊加得到最終預(yù)測(cè)值.仿真結(jié)果表明,該模型具有較高的預(yù)測(cè)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(jì)劃項(xiàng)目(122102210102)
【分類號(hào)】：P182.41

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