Full Text:   <2068>

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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: 2762

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wen-yang Duan

http://orcid.org/0000-0002-7811-4986

Li-min Huang

http://orcid.org/0000-0002-7944-2754

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

http://doi.org/10.1631/jzus.A1500040


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",
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pages="562-576",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1500040"
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%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
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500040

TY - JOUR
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
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1500040


Abstract: 
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.

用于非线性非平稳船舶运动极短期预报的一种复合自回归经验模态分解支持向量机回归模型

目的:基于支持向量机回归(SVR)模型在非线时间序列的预测能力及经验模态分解(EMD)方法在处理非线性非平稳性的优势,提出一种复合自回归经验模态分解支持向量机回归(AR-EMD-SVR)模型,提高非线性非平稳船舶运动极短期预报精度。
创新点:1.研究非线性非平稳船舶运动的极短期预报问题,提出一种复合的预报方法;2.基于不同层次的预报模型和模型试验数据,分析非线性非平稳性对极短期预报精度的影响。
方法:1.在SVR模型中引入基于自回归(AR)预报端点延拓的EMD方法,形成复合的AR-EMD-SVR预报模型;2.基于集装箱船模水池试验运动数据将AR-EMD-SVR模型与AR、SVR和EMD-AR三种模型进行比较,分析非线性非平稳性对极短期预报的影响以及不同模型的预报性能。
结论:1.AR-EMD方法能够有效的克服非平稳对极短期预报模型(AR和SVR)在精度上所带来的不良影响;2.基于船模试验数据的预报结果表明:相较于AR、SVR和EMD-AR三种预报模型,基于AR-EMD-SVR模型的非线性非平稳船舶运动极短期预报结果具有更高的精度。

关键词:非线性非平稳船舶运动;极短期预报;经验模态分解;支持向量机回归模型;自回归模型

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

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