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非平稳时序分析法在隧道施工变形预测中的应用 被引量:5
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作者 杨伟超 彭立敏 +1 位作者 黄娟 赵丹 《郑州大学学报(工学版)》 CAS 2008年第1期132-135,共4页
根据隧道施工变形的时空效应,提出基于求和型的自回归滑动平均AR IMA(p,d,q)计算模型的隧道变形预测方法.采用差分运算,对现场监测数据平稳性化处理,通过自、偏自相关函数的分析确定计算模型,采用AIC准则确定模型的阶次,最后通过最小二... 根据隧道施工变形的时空效应,提出基于求和型的自回归滑动平均AR IMA(p,d,q)计算模型的隧道变形预测方法.采用差分运算,对现场监测数据平稳性化处理,通过自、偏自相关函数的分析确定计算模型,采用AIC准则确定模型的阶次,最后通过最小二乘法对模型参数进行估计.工程应用表明,非平稳时序分析法的预报值与实际沉降量的平均相对误差为8.28%,最大绝对误差为0.45 mm,说明了采用非平稳时序分析法对隧道施工变形进行短期预报和实时监控是可行的. 展开更多
关键词 隧道 变形 非稳定时序 ARMA 预测
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The Study of a New Method for Forecasting Non-stationary Series
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作者 陈萍 Zhang Jie 《High Technology Letters》 EI CAS 2002年第2期47-50,共4页
A new method for forecasting non stationary series is developed. Its steps are as follows: Step 1. Data delaminating. Non stationary series is delaminated into several multi scale steady data layers and one trend laye... A new method for forecasting non stationary series is developed. Its steps are as follows: Step 1. Data delaminating. Non stationary series is delaminated into several multi scale steady data layers and one trend layer. Step 2. Modeling and forecasting each stationary data layer. Step 3. Imitating trend layer using polynomial. Step 4. Combining the forecasting layers and imitating layer into one series. The EMD (Empirical Mode Decomposition) method suitable to process non stationary series is selected to delaminate data, while ARMA (Auto Regressive Moving Average) model is employed to model and forecast stationary data layer and least square error method for trend layer regression. Aiming at forecasting length, forecasting orientation and selective method, experiments are performed for SAR (Synthetic Aperture Radar) images. Finally, an example is provided, in which the whole SAR image is restored via the method proposed by this paper. 展开更多
关键词 non stationary series forecasting data delaminating ARMA model EMD SAR image
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