An improved support vector regression and its modelling of manoeuvring performance in multidisciplinary ship design optimization
An improved support vector regression and its modelling of manoeuvring performance in multidisciplinary ship design optimization
In this paper, the combination of the Laplace loss function and Support Vector Regression (SVR) are presented for the estimation of manoeuvring performance in multidisciplinary ship design optimization, and a new SVR algorithm was proposed, which has only one parameter to control the errors and automatically minimized with v, and adds b2/2 b to the item of confidence interval. 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 about the offshore support vessel; the Latin Hypercube Design is employed to explore the design space. Instead of requiring the evaluation of expensive simulation codes, we establish the metamedels of ship manoeuvring performance; all the numerical results show the effectiveness and practicability of the new approximation algorithms.
metamodel, support vector machine, multidisciplinary design optimization, ship manoeuvring
122-128
Li, Dongqin
70fd2197-8e66-4b8d-bc3d-c74d527a72b7
Wilson, Philip A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Jiang, Zhiyong
fa53c4b0-ce87-4027-9d01-171af961139e
3 February 2016
Li, Dongqin
70fd2197-8e66-4b8d-bc3d-c74d527a72b7
Wilson, Philip A.
8307fa11-5d5e-47f6-9961-9d43767afa00
Jiang, Zhiyong
fa53c4b0-ce87-4027-9d01-171af961139e
Li, Dongqin, Wilson, Philip A. and Jiang, Zhiyong
(2016)
An improved support vector regression and its modelling of manoeuvring performance in multidisciplinary ship design optimization.
International Journal of Modelling and Simulation, 35 (3/4), .
(doi:10.1080/02286203.2015.1111055).
Abstract
In this paper, the combination of the Laplace loss function and Support Vector Regression (SVR) are presented for the estimation of manoeuvring performance in multidisciplinary ship design optimization, and a new SVR algorithm was proposed, which has only one parameter to control the errors and automatically minimized with v, and adds b2/2 b to the item of confidence interval. 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 about the offshore support vessel; the Latin Hypercube Design is employed to explore the design space. Instead of requiring the evaluation of expensive simulation codes, we establish the metamedels of ship manoeuvring performance; all the numerical results show the effectiveness and practicability of the new approximation algorithms.
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Submitted date: 20 May 2014
e-pub ahead of print date: 20 October 2015
Published date: 3 February 2016
Keywords:
metamodel, support vector machine, multidisciplinary design optimization, ship manoeuvring
Organisations:
Fluid Structure Interactions Group
Identifiers
Local EPrints ID: 383129
URI: http://eprints.soton.ac.uk/id/eprint/383129
ISSN: 0228-6203
PURE UUID: b65d6104-fe22-4854-b9e2-3da383d6458e
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Date deposited: 02 Nov 2015 16:29
Last modified: 15 Mar 2024 02:35
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Author:
Dongqin Li
Author:
Zhiyong Jiang
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