【摘要】：分別運(yùn)用經(jīng)驗(yàn)?zāi)B(tài)分解(empirical mode decomposition,EMD)法和快速集合經(jīng)驗(yàn)?zāi)B(tài)分解(fast ensemble empirical mode decomposition,FEEMD)法將非平穩(wěn)下?lián)舯┝黠L(fēng)速分解為一系列穩(wěn)態(tài)序列集,即固有模態(tài)分量.建立極限學(xué)習(xí)機(jī)(extreme learning machines,ELM)風(fēng)速預(yù)測(cè)模型(EMD-ELM)和快速EMD-ELM(FEEMD-ELM),分別對(duì)分解后的非平穩(wěn)脈動(dòng)風(fēng)速訓(xùn)練集和測(cè)試集進(jìn)行預(yù)測(cè).同時(shí),將EMD和FEEMD與基于粒子群優(yōu)化(particle swarm optimization,PSO)最小二乘支持向量機(jī)(least squares support vector machine,LSSVM)進(jìn)行混合,形成EMD-PSO-LSSVM和FEEMD-PSO-LSSVM混合模型算法.通過比較這4種預(yù)測(cè)算法的結(jié)果發(fā)現(xiàn),基于EMD-ELM和FEEMD-ELM的非平穩(wěn)下?lián)舯┝黠L(fēng)速預(yù)測(cè)模型更為準(zhǔn)確高效,其中FEEMD-ELM模型預(yù)測(cè)最佳.
[Abstract]:The empirical mode decomposition (empirical mode decomposition,EMD) method and the fast set empirical mode decomposition (fast ensemble empirical mode decomposition,FEEMD) method are used to decompose the wind speed of non-stationary downburst flow into a series of steady state sequence sets, namely the intrinsic modal component. A wind speed prediction model (EMD-ELM) and a fast EMD-ELM (FEEMD-ELM) model for extreme learning machine (extreme learning machines,ELM) were established to predict the non-stationary pulsating wind speed training set and the test set, respectively. At the same time, the EMD and FEEMD are mixed with the least squares support vector machine (least squares support vector machine,LSSVM) based on particle swarm optimization (particle swarm optimization,PSO) to form the EMD-PSO-LSSVM and FEEMD-PSO-LSSVM hybrid model algorithm. By comparing the four prediction algorithms, it is found that the model based on EMD-ELM and FEEMD-ELM is more accurate and efficient, and the FEEMD-ELM model is the best.
【作者單位】： 上海大學(xué)土木工程系
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51378304)
【分類號(hào)】：TP18

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1 鐘旺;李春祥;;基于極限學(xué)習(xí)機(jī)的非平穩(wěn)下?lián)舯┝黠L(fēng)速預(yù)測(cè)[J];上海大學(xué)學(xué)報(bào)(自然科學(xué)版);2018年03期

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