The present study investigates the use of Artificial Neural Networks (ANNs) for the resistance prediction of hullforms designed according to the MARAD Systematic Series. Experimental data for the residual resistance coefficient of these hulls, provided by MARAD in a series of diagrams, have been used to train and evaluate a series of neural networks aiming to estimate the residual resistance coefficient of ships designed according to the MARAD Series. The adopted procedure along with the obtained results are presented and discussed.
Prediction of Resistance of MARAD Systematic Series’ Hullforms using Artificial Neural Networks
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Margari, Vasiliki, Kanellopoulou, Aphrodite, and George Zaraphonitis. "Prediction of Resistance of MARAD Systematic Series’ Hullforms using Artificial Neural Networks." Paper presented at the SNAME 6th International Symposium on Ship Operations, Management and Economics, Athens, Greece, March 2018.
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