System-ldentification for Regular Water Waves

doi:10.3850/978-90-833476-1-5_iahr40wc-p1436-cd

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Title	System-ldentification for Regular Water Waves
Creator	Liang，Aoming 1,2; Zheng，Kun 3; Wang，Zhan 4,5; Li，Ruipeng 2; Ge，Mingming 2,6; Fan，Dixia 2
Date Issued	2023
Source Publication	Proceedings of the IAHR World Congress
ISSN	2521-7119
Pages	2186-2191
Abstract	In this paper, we study and estimate the regular water-wave identification methods by use of the Nonlinear auto-regressive model (NARM) and Hammerstein-Wiener model. We analyze and optimize the parameters in multi-regressions. Under the nonlinear group regression model, we selected three common models, such as wavelet transform, decision tree model, and support vector machine model with Gaussian process. Finally, the Hammerstein-Wiener shows a great performance on identification processes. Specifically, we achieve a maximum accuracy of 88% on our validation set. We used the AIC index and NMSE to measure the superiority ofthe model.
Keyword	Hammerstein-Wiener model Nonlinearauto-regressive model System identification Waveprediction
DOI	10.3850/978-90-833476-1-5_iahr40wc-p1436-cd
URL	View source
Language	英语English
Scopus ID	2-s2.0-85187705965
Citation statistics
Document Type	Conference paper
Identifier	http://repository.uic.edu.cn/handle/39GCC9TT/12295
Collection	Research outside affiliated institution
Affiliation	1.Zhejiang University,Hangzhou,China 2.Key Laboratory of Coastal Environment and Resources of Zhejiang Province,Westlake University,Hangzhou,China 3.China Ship Scientific Research Center,Wuxi,China 4.Harbin Engineering University,Harbin,China 5.Qingdao Innovation and Development Center of Harbin Engineering University,Qingdao,China 6.Macau University of Science and Technology,MSARC,China
Recommended Citation GB/T 7714	Liang，Aoming,Zheng，Kun,Wang，Zhanet al. System-ldentification for Regular Water Waves[C], 2023: 2186-2191.

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