A flexible system for initial ship design parameters estimation using a system of neural networks

Dublin Core

Title

A flexible system for initial ship design parameters estimation using a system of neural networks

Subject

Ship design
regression
ship series
Artificial Neural Networks (ANNs)
Multilayer Perceptrons (MLPs)
Normalized Gaussian Modified Lagrangian (NGML)

Description

To initialize ship design process, it is very important to be able to develop an initial estimate of ship parameters to satisfy designer required specifications. For new emerging designs, this estimate has to be made based on a limited available set of examples. Moreover, a practical estimate prediction strategy should be flexible enough having no distinction between input (specified constraints) and outputs (parameters required to be estimated), since these vary from one design case to another.  Conventional regression-based techniques, which are usually employed to provide the required estimates, suffer from low accuracy in case of a small number of available examples. In addition to that, they fail to capture the interrelation between different design parameters. To overcome these limitations and others, the present paper proposes a new approach based on a system of artificial neural-networks (ANNs). The new approach not only overcomes regression limitations but is also capable of providing a reliable estimate of initial design offset table based on different ANN outputs.  The paper uses a case study for demonstrating the merits of the proposed approach.Keywords: Ship design; regression; ship series; Artificial Neural Networks (ANNs); Multilayer Perceptrons (MLPs); Normalized Gaussian Modified Lagrangian (NGML) doi: http://dx.doi.org/10.3329/jname.v8i2.6945 Journal of Naval Architecture and Marine Engineering 8(2011) 71-82

Creator

Senousy, Hamada
Abou-Elmakarem, Mahmoud

Source

Journal of Naval Architecture and Marine Engineering; Vol. 8 No. 2 (2011); 71-82
2070-8998
1813-8535

Publisher

Association of Naval Architects and Marine Engineers

Date

2011-11-29

Relation

Format

application/pdf

Language

eng

Type

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

Identifier

Citation

Hamada Senousy and Abou-Elmakarem, Mahmoud, A flexible system for initial ship design parameters estimation using a system of neural networks, Association of Naval Architects and Marine Engineers, 2011, accessed November 7, 2024, https://igi.indrastra.com/items/show/3204

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