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Assessment of Manoeuvring Performance and Metamodels in Multidisciplinary Ship Design Optimization

Assessment of Manoeuvring Performance and Metamodels in Multidisciplinary Ship Design Optimization
Assessment of Manoeuvring Performance and Metamodels in Multidisciplinary Ship Design Optimization
In this paper, the combination of Laplace loss function and Support Vector Regression (SVR) are presented for the estimation of manoeuvring performance in multidisciplinary ship design optimization, and it is named as Single-parameter Lagrangian Support Vector Regression (SPL-SVR) which has only one parameter to control the errors and adds b2/2 to the item of confidence interval at the same time. It is shown that the proposed SVR algorithm in conjunction with the Laplace loss function can estimate the ship manoeuvring performance appropriately compared to the simulation results with Napa software and other approximation methods such as Artificial Neural Network (ANN) and classic SVR. In this article, we also gather enough ship information.
metamodel, support vector machine, multidisciplinary design optimization, ship manoeuvring
1007-7294
243-257
Li, Dongqin
70fd2197-8e66-4b8d-bc3d-c74d527a72b7
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Guan, Yifeng
dd54db9c-2a8e-4153-8ce9-5978504b1d0a
Li, Dongqin
70fd2197-8e66-4b8d-bc3d-c74d527a72b7
Wilson, P.A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Guan, Yifeng
dd54db9c-2a8e-4153-8ce9-5978504b1d0a

Li, Dongqin, Wilson, P.A. and Guan, Yifeng (2015) Assessment of Manoeuvring Performance and Metamodels in Multidisciplinary Ship Design Optimization. Journal of Ship Mechanics, 20 (3), 243-257. (doi:10.3969/j.issn.1007-7294.2015.12.003).

Record type: Article

Abstract

In this paper, the combination of Laplace loss function and Support Vector Regression (SVR) are presented for the estimation of manoeuvring performance in multidisciplinary ship design optimization, and it is named as Single-parameter Lagrangian Support Vector Regression (SPL-SVR) which has only one parameter to control the errors and adds b2/2 to the item of confidence interval at the same time. It is shown that the proposed SVR algorithm in conjunction with the Laplace loss function can estimate the ship manoeuvring performance appropriately compared to the simulation results with Napa software and other approximation methods such as Artificial Neural Network (ANN) and classic SVR. In this article, we also gather enough ship information.

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一种有效近似建模方法及船舶耐波性代理模型构建_英文_李冬琴.pdf - Other
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Submitted date: 20 October 2014
Accepted/In Press date: 15 January 2015
Published date: 30 December 2015
Keywords: metamodel, support vector machine, multidisciplinary design optimization, ship manoeuvring
Organisations: Fluid Structure Interactions Group

Identifiers

Local EPrints ID: 373543
URI: http://eprints.soton.ac.uk/id/eprint/373543
ISSN: 1007-7294
PURE UUID: 778a099a-dd78-458e-a125-2b51cea5a7c3
ORCID for P.A. Wilson: ORCID iD orcid.org/0000-0002-6939-682X

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Date deposited: 21 Jan 2015 15:32
Last modified: 09 Jan 2022 02:34

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Contributors

Author: Dongqin Li
Author: P.A. Wilson ORCID iD
Author: Yifeng Guan

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