Prediction Model for Nonlinear Deformation Time Series

DOI

10.2991/isci-15.2015.299How to use a DOI?

Keywords

deformation; Hilbert–Huang transform; EMD; multi-scale characteristics; prediction model

Abstract

In this paper, we applied the Hilbert–Huang transform method to improve the accuracy of nonlinear deformation predictions. We propose a nonlinear model for prediction based on the multi-scale characteristics of a signal, and used the empirical mode decomposition (EMD) method to decompose the signal. We first applied our method to a simulation of the Lorenz system. Our results show that the EMDs have smaller largest Lyapunov indices than the original signal. We can use this to determine the maximum prediction time for a nonlinear signal. We then constructed a new model based on EMD signals. The results of our experiment demonstrated that this prediction accuracy is perfect. Finally, we used the characteristics of the EMD signals to build the EMD-LLSVM prediction model. Our results show that this model is more accurate than traditional models.

Copyright

© 2015, 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  - Xinxia Liu
AU  - Shengli Li
AU  - Yujie Zhang
AU  - Yantao Yang
AU  - Tianyang Chen
PY  - 2015/01
DA  - 2015/01
TI  - Prediction Model for Nonlinear Deformation Time Series
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 2290
EP  - 2299
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.299
DO  - 10.2991/isci-15.2015.299
ID  - Liu2015/01
ER  -