本文選題：海洋表面溫度 + 經(jīng)驗?zāi)B(tài)分解�。� 參考：《數(shù)學(xué)的實踐與認識》2017年07期

【摘要】：海洋表面溫度(SST)具有非線性、非平穩(wěn)等特征,給處理和預(yù)測帶來了很大的困難.將集合經(jīng)驗?zāi)B(tài)分解(EEMD)、改進的集合經(jīng)驗?zāi)B(tài)分解(CEEMD)與支持向量機(SVM)方法相結(jié)合,實現(xiàn)了對東北太平洋月平均海溫距平序列(SSTA)的預(yù)測:首先應(yīng)用EEMD或CEEMD方法將SST數(shù)據(jù)分解為多個本征模態(tài)函數(shù)(IMFs),然后應(yīng)用SVM算法對各IMFs進行擬合、預(yù)測,最后對各IMFs預(yù)測結(jié)果疊加重構(gòu)得到預(yù)測結(jié)果.EEMD-SVM和CEEMD-SVM數(shù)值模擬結(jié)果顯示,預(yù)測最大誤差小于0.25℃,并且CEEMD-SVM預(yù)測效果更好,為SST實際預(yù)測提供了參考.
[Abstract]:Ocean surface temperature (SST) has the characteristics of nonlinearity and nonstationarity, which makes it difficult to deal with and predict. This paper combines set empirical mode decomposition (EMD), improved set empirical mode decomposition (EMD) and support vector machine (SVM) method. The prediction of the monthly mean SST anomaly sequence in the Northeast Pacific Ocean is realized. Firstly, the SST data are decomposed into multiple intrinsic mode functions by EEMD or CEEMD, and then the IMFs is fitted and predicted by the SVM algorithm. Finally, the prediction results are obtained by superposition reconstruction of IMFs prediction results. EEMD-SVM and CEEMD-SVM numerical simulation results show that the maximum error of prediction is less than 0.25 鈩，

本文編號：1786087