基于EMD和LS-SVM的复合地基沉降预测

针对复合地基沉降量时间序列呈现出的非线性、非平稳、多尺度的特点,提出了一种结合经验模式分解(EMD)和最小二乘支持向量机(LS-SVM)的复合地基沉降量预测模型。通过EMD分解,将原始观测数据沉降量的非平稳序列分解为若干不同尺度的平稳IMF(固有模态分量)分量,应用LS-SVM对各IMF分量分别进行预测,然后再组合得到复合地基沉降数据的预测结果。结合工程实例进行了实际应用,该方法不仅能够获得较高的预测精度,还能够反映原始沉降量数据的突变性,表明此方法具有推广价值。

The Settlement Prediction of composite foundation Based on EMD and LS-SVM

According to a series of features such as nonlinear, non-stationary, multi-scale exhibited from composite foundation settlement time sequences, a composite foundation settlement prediction model , combined of empirical mode decomposition (EMD) and least squares support vector machine (LS-SVM),is proposed. By EMD decomposition, the non-stationary sequence of the settlement originating from observational data is decomposed into several different dimensions smooth IMF (intrinsic mode components) components, of which each is predicted by the LS-SVM, which will eventually combine those components to reach the forecast results about the original settlement data sequence. Verified in the practical application of engineering example, the method can not only obtain high prediction accuracy, but reflect the mutation of the original settlement data, both of the two indicating that this method is of promotional value.