一种解决经验模态分解端点效应的边界延拓法

doi:10.7527/S1000-6893.2015.0111

Abstract

Abstract:

The data used for individual or systematic radiation emission prediction always has the features like non-linear regularity and small sample volume, which provide significant difficulties for accurate model establishment. However, by introducing the empirical mode decomposition(EMD) method, the non-linear and non-stationary data can be decomposed into several periodic intrinsic mode functions(IMF). The advantages of the EMD method including completeness and orthogonaity enable us to decompose the modeling of initial data into the modeling of IMFs components. However, due to the characteristics of the electromagnetic compatibility test data, EMD suffers from the end effect which limits its precision. In order to enhance the accuracy of EMD, this paper presents a novel approach which is based on the mean gray GM(1, 1) prediction model with end-point extension. Specifically, based on the fact that the data volume required by mean gray GM(1, 1) prediction model is relatively small, the maximum and minimum values are added at each end point of the initial data set respectively to suppress the end effect of EMD method. Simulation results indicate that decomposition layer number and the average relative error are optimized significantly. Furthermore, the required sample data volume can be reduced much significantly than the existing EMD methods.

Key words: radiation emission, trend prediction, empirical mode decomposition, end effect, mean grey GM(1, 1) prediction model

CLC Number:

V19
TN01

SU Donglin, ZHENG Haopeng. A boundary extension method for empirical mode decomposition end effect[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(3): 960-969.

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A boundary extension method for empirical mode decomposition end effect

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