A flexible system for initial ship design parameters estimation using a system of neural networks
DOI:
https://doi.org/10.3329/jname.v8i2.6945Keywords:
Ship design, regression, ship series, Artificial Neural Networks (ANNs), Multilayer Perceptrons (MLPs), Normalized Gaussian Modified Lagrangian (NGML)Abstract
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
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