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 auto...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2016-02-03.
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| Subjects: | |
| Online Access: | Get fulltext Get fulltext |
| Summary: | 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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