Full Text:   <2005>

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CLC number: U66

On-line Access: 2015-07-03

Received: 2015-03-02

Revision Accepted: 2015-05-25

Crosschecked: 2015-06-12

Cited: 6

Clicked: 2669

Citations:  Bibtex RefMan EndNote GB/T7714


Wen-yang Duan


Li-min Huang


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Journal of Zhejiang University SCIENCE A 2015 Vol.16 No.7 P.562-576


A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

Author(s):  Wen-yang Duan, Li-min Huang, Yang Han, Ya-hui Zhang, Shuo Huang

Affiliation(s):  Department of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China; more

Corresponding email(s):   huanglimin@hrbeu.edu.cn

Key Words:  Nonlinear and non-stationary ship motion, Short-term prediction, Empirical mode decomposition (EMD), Support vector regression (SVR) model, Autoregressive (AR) model

Wen-yang Duan, Li-min Huang, Yang Han, Ya-hui Zhang, Shuo Huang. A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion[J]. Journal of Zhejiang University Science A, 2015, 16(7): 562-576.

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author="Wen-yang Duan, Li-min Huang, Yang Han, Ya-hui Zhang, Shuo Huang",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion
%A Wen-yang Duan
%A Li-min Huang
%A Yang Han
%A Ya-hui Zhang
%A Shuo Huang
%J Journal of Zhejiang University SCIENCE A
%V 16
%N 7
%P 562-576
%@ 1673-565X
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500040

T1 - A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion
A1 - Wen-yang Duan
A1 - Li-min Huang
A1 - Yang Han
A1 - Ya-hui Zhang
A1 - Shuo Huang
J0 - Journal of Zhejiang University Science A
VL - 16
IS - 7
SP - 562
EP - 576
%@ 1673-565X
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1500040

Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector regression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends extension. In addition to the AR-EMD-SVR model, the linear AR model, non-linear SVR model, and hybrid EMD-AR model are also studied for comparison by using ship motion time series obtained from model testing in a towing tank. Prediction results suggest that the non-stationary difficulty in the SVR model is overcome by using the AR-EMD technique, and better predictions are obtained by the proposed AR-EMD-SVR model than other models.

This is a very good article and the authors are to be congratulated.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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