基于KPCA-ICFSFDP-MOGP的拱坝多测点变形数据预处理及预测方法

为更高效地对高拱坝的多测点变形数据进行分析,引入了核主成分分析(KPCA)算法重构误差以识别多测点序列中的异常值;为解决用于测点聚类的密度峰值聚类(CFSFDP)算法高维表现较差的问题,利用KPCA算法对其进行降维操作,并提出了自动选取聚类中心与截断距离的改进CFSFDP(ICFSFDP)算法;基于KPCA-ICFSFDP和多输出高斯过程(MOGP)算法,按分区建立了多测点变形MOGP回归模型。实例验证结果表明,对于簇内点数量较少的类,相较于不分区的MOGP模型,预测效果得到了一定的提升,同时在整体MOGP模型表现良好的测点,分区后仍然保持较高的预测精度,且与单输出高斯过程模型对比均有所提升。

Preprocessing and prediction method for multi-point deformation data of arch dams based on KPCA-ICFSFDP-MOGP

In order to analyze the multi-point data of high arch dams more efficiently, the kernel principal component analysis (KPCA) method for error reconstruction was introduced to identify the outliers in the multi-point sequence. To solve the problem of poor performance of the clustering by fast search and find of density peaks (CFSFDP) algorithm in high dimension, the KPCA method was used to reduce the dimension, and an improved CFSFDP (ICFSFDP) algorithm was proposed for automatic selection of cluster center and truncation distance. Based on KPCA-ICFSFDP method and the multiple output Gaussian process (MOGP) method, a multi-point deformation MOGP model was built according to the partition. The results show that for the small number of partitions within the cluster points, compared with the MOGP model without partition, the prediction effect is improved. Meanwhile, for the measurement points with good performance of the overall MOGP model, the prediction accuracy remains high after partitioning, which is improved compared with the single-output Gaussian process model.