传感器布置不足和传感器数据缺失是风压实测研究中需要解决的重要问题，风压的空间预测可以恢复缺失数据和拓展风压空间信息，帮助建立结构表面的风压分布.为此提出一种基于多变量经验模态分解(MEMD)和极限学习机(ELM)的空间预测算法.采用MEMD分解非平稳信号，得到多组模态数目相同且频率匹配的固有模态函数和余项.对分解得到的数据按频率进行重组，作为输入数据，用ELM进行学习和预测.采用基于自回归滑动平均的模拟风速数据和实测非平稳风压数据来验证算法的有效性和精确度，同时引入基于径向基核函数的最小二乘支持向量机(RBF-LSSVM)和ELM方法作为对比.试验结果表明，MEMD-ELM方法的预测结果误差更小，与真实值更为接近.MEMD的多变量同时分解可以保留数据间的相关性，从而在非平稳过程空间预测时达到更好的效果，是一种稳定而有效的多变量预测方法.
The lack of sensor layout and the deficiency of sensor data are the key problems in the study of wind pressure measurement. The spatial prediction of wind pressure can restore the missing data and expand the air pressure information, and help to establish the wind pressure distribution on the structure surface. In this paper, a spatial prediction algorithm based on multivariate empirical mode decomposition (MEMD) and extreme learning machine (ELM) is proposed. The MEMD method is used to decompose the multi-channel non-stationary signals. The intrinsic mode functions and the residue are obtained with the same number and similar frequency. The decomposed data are restructured by frequency and ELM is used to train the restructured data and make predictions. The effectiveness and accuracy of the algorithm are verified by simulated data from autoregressive moving average (ARMA) model and observed wind pressure data. At the same time, the least squares support vector machine using radial basis kernel function (RBF-LSSVM) and the basic ELM method are introduced as the comparison. It is proved that the prediction error of the MEMD-ELM method is smaller and the correlation with the real value is higher. The modes decomposed through MEMD preserve the correlation from origin data, so a more accurate result is obtained, which proves that MEMD-ELM is an effective multi-variety prediction method.
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