| 基于EEMD的大坝变形多步预测方法研究 |
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| 引用本文： | 任超,梁月吉,庞光锋,蓝岚,唐利.基于EEMD的大坝变形多步预测方法研究[J].大地测量与地球动力学,2015,35(5):816-820. |
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| 作者姓名： | 任超 梁月吉 庞光锋 蓝岚 唐利 |
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| 摘 要： | 针对大坝变形具有非线性和非平稳性的特点,提出一种基于集合经验模态分解(EEMD)的大坝变形多步预测新算法。首先从时频分析出发,利用集合经验模态分解将变形时间序列分解成具有不同频率特征的分量;然后采用游程判定法对波动程度相似的分量重构为高、中和低频3个分量;最后对3个分量分别建立相应的多步预测模型,叠加各预测值即为最终预测结果。经算例验证,并与AR模型、BP神经网络和支持向量机的多步预测进行对比分析,同时建立不同预测步长进一步验证。结果表明,该算法预测精度较高,在大坝变形波动剧烈的时段也能保证较好的预测效果,可以应用于大坝变形预测。
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| 关 键 词： | 大坝变形 集合经验模态分解 支持向量机 AR模型 多步预测 |
A Novel Multi-Step Prediction for Dam Deformation Prediction Based on EEMD |
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| REN Chao,LIANG Yueji,PANG Guangfeng,LAN Lan,TANG Li.A Novel Multi-Step Prediction for Dam Deformation Prediction Based on EEMD[J].Journal of Geodesy and Geodynamics,2015,35(5):816-820. |
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| Authors: | REN Chao LIANG Yueji PANG Guangfeng LAN Lan TANG Li |
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| Abstract: | Multi-step prediction, a new algorithm based on ensemble empirical mode decomposition (EEMD) for dam deformation prediction, is presented. Firstly, starting from the time-frequency analysis, with the use of a collection of empirical mode decomposition, deformation time series are broken down into characteristic components of different frequencies. Secondly, run-determination act is used to reconstruct volatility components similar to high, medium and low ones. This is effective in centralizing model predictive feature information and reducing the difficulty. Finally, multi-step prediction model of the three is established separately and the predictive values are overlayed as the final prediction result. The calculation result is analyzed and compared with the AR model, BP neural network and SVM. At the same time, different prediction verification instructions are established to prove our algorithm. The results show that the prediction accuracy of EEMD is higher, it can guarantee better prediction of dam deformation in volatile periods| it is feasible to apply to dam deformation prediction. |
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| Keywords: | dam deformation ensemble empirical mode decomposition support vector machines AR model multi-step prediction |
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