SIA OpenIR  > 海洋机器人卓越创新中心
Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders
Zhou YJ(周耀鉴); Yu JC(俞建成); Wang XH(王晓辉)
Department海洋机器人卓越创新中心
Source PublicationIEEE ACCESS
ISSN2169-3536
2017
Volume5Pages:5773-5384
Indexed BySCI ; EI
EI Accession number20173504088091
WOS IDWOS:000401431300066
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China [61233013]
KeywordUnderwater Glider Time Series Depth-averaged Current Velocities (Dacvs)
AbstractIn this paper, we propose time series prediction methods for depth-averaged current velocities (DACVs) of underwater gliders. Based on historical DACV data, these methods can predict the DACVs of future profiles with good performance. Regarding DACVs as time series, we use backpropagation neural network and least squares support vector machine (LSSVM) methods to predict the DACVs. To obtain better prediction performance, the features of DACVs are considered, and we use empirical mode decomposition (EMD) to decompose the time series into several sub-series. Then, the two methods are reused to predict each sub-series, and the results of all the sub-series with each method are added. Based on the real-time DACVs obtained from the simulation environment and the DACVs obtained from sea trials, we test and verify the four methods. The results demonstrate that all the methods exhibit a good prediction performance for conditions in which ocean currents are relatively regular; whereas in other cases, EMD-LSSVM shows inherent robustness and superiority compared with the other three methods.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordMONTEREY BAY
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20499
Collection海洋机器人卓越创新中心
Corresponding AuthorYu JC(俞建成)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Zhou YJ,Yu JC,Wang XH. Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders[J]. IEEE ACCESS,2017,5:5773-5384.
APA Zhou YJ,Yu JC,&Wang XH.(2017).Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders.IEEE ACCESS,5,5773-5384.
MLA Zhou YJ,et al."Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders".IEEE ACCESS 5(2017):5773-5384.
Files in This Item: Download All
File Name/Size DocType Version Access License
Time Series Predicti(7528KB)期刊论文作者接受稿开放获取ODC PDDLView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhou YJ(周耀鉴)]'s Articles
[Yu JC(俞建成)]'s Articles
[Wang XH(王晓辉)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhou YJ(周耀鉴)]'s Articles
[Yu JC(俞建成)]'s Articles
[Wang XH(王晓辉)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhou YJ(周耀鉴)]'s Articles
[Yu JC(俞建成)]'s Articles
[Wang XH(王晓辉)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Time Series Prediction Methods for Depth-Averaged Current Velocities of Underwater Gliders.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.