Study on methods for improving LMD end effect by Gaussian Process based on the particle swarm optimization algorithm

DOI

10.2991/icmmse-16.2016.63How to use a DOI?

Keywords

Local mean decomposition, End effect, Particle swarm optimization, Gaussian process, Mechanical vibration signal

Abstract

The LMD is a new method for analyzing non-stationary signals. It can decompose complicated signals into a set of single-component signals, each of which has physical sense. However, performing the LMD will produce end effects which make results distorted. After analyzing the reasons for these, the article takes advantage of the Gaussian process algorithm to overcome the end effects of LMD. To improve the precision of GP algorithm of endpoint extension, the authors use the particle swarm algorithm to optimization the GP hyper parameter and select the optimal covariance function. Experimental results showed that the GP algorithm of particle swarm optimization (PSO) can predict the two ends of the data signal more accurately, improve the accuracy of LMD and avoid the adverse effects caused by end effect according to the internal characteristics of the signal. Therefore the PSO-GP algorithm is a better method to improve the end effect.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yiyong Luo
AU  - Qicai Chi
AU  - Yunqi Zhou
AU  - Shijian Zhou
PY  - 2016/03
DA  - 2016/03
TI  - Study on methods for improving LMD end effect by Gaussian Process based on the particle swarm optimization algorithm
BT  - Proceedings of the 2016 International Conference on Mechanics, Materials and Structural Engineering
PB  - Atlantis Press
SP  - 373
EP  - 384
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmse-16.2016.63
DO  - 10.2991/icmmse-16.2016.63
ID  - Luo2016/03
ER  -