Restrain end-effect in empirical mode decomposition by mirror extension and radial basis function neural network prediction

	Restrain end-effect in empirical mode decomposition by mirror extension and radial basis function neural network prediction
	Han, Jianping 1; Qian, Jiongn 2
	2010
会议日期	April 23, 2010 - April 25, 2010
会议地点	Yantai, China
关键词	Forecasting Interpolation Mathematical transformations Mirrors Radial basis function networks Reinforced concrete Acceleration signals Empirical Mode Decomposition End effects Hilbert Huang transforms Nonlinear and non-stationary signals Radial basis function neural networks Reinforced concrete frames Spline interpolation
卷号	1
页码	375-381
英文摘要	Hilbert-Huang Transform (HHT) is a new powerful signal processing approach, especially for nonlinear and non-stationary signal. HHT consists of Empirical Mode Decomposition (EMD) and Hilbert Transform (HT) and EMD is the crucial step. However, there is a troublesome end-effect issue to apply spline interpolation to get the upper and lower envelopes. Based on discussion of cause and influence of end-effect, mirror extension and prediction via radial basis function (RBF) neural network are investigated to restrain end-effect in EMD. Then a simulated signal and a recorded acceleration signal from shaking table test of a 12-stroey reinforced concrete frame model are preprocessed by these methods and decomposed by EMD respectively. The results indicate that the proposed methods can restrain end-effect to some extent, but still can not completely eliminate end-effect in EMD. In addition, for complex signal such as acceleration record of a structure, only extending signal by RBF neural network is not effective to restrain end-effect in EMD. But applying mirror extension after predicting the filtered signal using RBF neural network can restrain end-effect in EMD effectively.
会议录	Proceedings of the 12th International Conference on Inspection, Appraisal, Repairs and Maintenance of Structures
会议录出版者	CI-Premier Pte Ltd
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/116836]
专题	教务处（创新创业学院）
作者单位	1.Lanzhou University of Technology, China; 2.Planning and Construction Bureau of Anji County, China
推荐引用方式 GB/T 7714	Han, Jianping,Qian, Jiongn. Restrain end-effect in empirical mode decomposition by mirror extension and radial basis function neural network prediction[C]. 见:. Yantai, China. April 23, 2010 - April 25, 2010.