基于EMD与RBF的隧道围岩监测数据分析.pdf

第34 卷第3 期 合肥工业大学学报( 自然 科 学 版 ) Vol. 34 No. 3 2011 年3 月 JOURNAL OF H EFEI UNIVERSITY OF TECH NOLOGY Mar. 2011 Doi: 10. 3969/j. issn. 1003 5060. 2011. 03. 018 EMD RBF 潘 龙, 王建国 ( , 230009) :, , , , , , , , RBF ; , , , :; ; ; :U4561 3 :A : 1003 5060( 2011) 03 0395 05 Analysis of monitoring data of wal-l rock in expressway tunnel based on EMD method and RBFneural network PAN Long, WANG Jian guo ( School of Civil and Hydraulic Engineering, Hefei University of T echnology, Hefei 230009, China) Abstract:Considering that many random errors are contained in the monitoring data of tunnel wall rock, a de noising method for the monitoring data is proposed based on the theory of empirical mode decomposition ( EMD) and singular spectrum analysis. The EMD method is used to decompose the time series into some in trinsic mode function components, by which the feature vector matrixes are formed. Then some important in trinsic mode functions are reconstructed by the determined optimal orders according to the result of singular spectrum analysis. As a result, the interference of random error is reduced or eliminated. As an example, the monitoring data of a tunnel wall rock is denoised by the proposed method. Pressure prediction for the original data and the denoised data are given by using radial basis function( RBF) neural network. The results show that this reasonable method can effectively distinguish the noise and useful information and it is particularly suitable for analyzing the monitoring data of tunnel wall rock. ey words:empirical mode decomposition( EMD) ; monitoring of wall rock; noise reduction; radial basis func tion( RBF) neural network , , ,