本文選題：滑坡位移預(yù)測(cè)　切入點(diǎn)：集成學(xué)習(xí)　出處：《華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年09期 　論文類型：期刊論文

【摘要】：提出了一種基于誤差修正在線貫序超限學(xué)習(xí)機(jī)集成(EOS-ELM)的滑坡位移預(yù)測(cè)模型.預(yù)測(cè)過程中對(duì)滑坡位移時(shí)間序列進(jìn)行了趨勢(shì)項(xiàng)和周期項(xiàng)分解,分別考慮了不同的影響因子對(duì)滑坡趨勢(shì)項(xiàng)位移和周期項(xiàng)位移的影響.利用在線貫序超限學(xué)習(xí)機(jī)(OS-ELM)算法分別對(duì)趨勢(shì)項(xiàng)位移和周期項(xiàng)位移建模預(yù)測(cè).采用集成預(yù)測(cè)的思想提高OS-ELM模型的泛化能力,同時(shí)為了進(jìn)一步提高預(yù)測(cè)精度,提出了一種在線誤差修正預(yù)測(cè)方法.該方法通過對(duì)誤差序列進(jìn)行建模預(yù)測(cè),修正最終的預(yù)測(cè)結(jié)果.以三峽庫(kù)區(qū)白水河滑坡為例,實(shí)驗(yàn)驗(yàn)證了提出方法的有效性.
[Abstract]:This paper presents a landslide displacement prediction model based on error correction online sequential overrun learning machine integrated with EOS-ELM. In the process of prediction, the trend term and period term of the time series of landslide displacement are decomposed. The influence of different influence factors on the displacement of the trend term and the periodic term of landslide is considered respectively. The method of on-line sequential over-limit learning machine (OS-ELM) is used to model and predict the displacement of the trend term and the periodic term, respectively. The integrated method is used to predict the displacement of the trend term and the periodic term. To improve the generalization ability of OS-ELM model, At the same time, in order to further improve the prediction accuracy, an online error correction prediction method is proposed. By modeling and forecasting the error series, the final prediction results are corrected. Taking the Baishui River landslide in the three Gorges Reservoir area as an example, The effectiveness of the proposed method is verified by experiments.
【作者單位】： 武漢理工大學(xué)自動(dòng)化學(xué)院;華中科技大學(xué)自動(dòng)化學(xué)院;中南民族大學(xué)計(jì)算機(jī)科學(xué)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61503144) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(2017IVA058)
【分類號(hào)】：P642.22
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本文編號(hào)：1623038