The application of ANN in estimating added resistance of container ships in regular head waves

Abstract

The determination of ship added resistance in waves requires rather complex hydrodynamic calculations to ensure an acceptable accuracy of the results. A model based on Artificial Neural Network (ANN) that allows simple but sufficiently accurate and reliable evaluation of the ship added resistance in regular head waves is proposed. The basis of the developed model are the results of hydrodynamic calculations of added resistance in regular waves, obtained based on the potential flow theory, and feedforward ANN with error back propagation. Based on the analysis of different ANN structures i.e., by varying the number of neurons in the hidden layer, an adequate ANN is set. The accuracy and generalization property of ANN is evaluated based on the normalized value of the root mean square error. The developed model can estimate the added resistance of container ships with sufficient accuracy, based on the ship characteristics and sailing speed.