Application of ensemble neural networks to prediction of towboat shaft power

In this paper towboat shaft power was predicted using various artificial neural networks. This work is a step toward reducing errors in the prediction of towboat power as well as providing better understanding of powering characteristics by the crew of the towboat. An ensemble neural network (ENN) and the single neural network (ANN) with two hidden layers are proposed to predict towboat shaft power. These two models were compared on the basis of their calculated root mean squared errors, mean absolute errors and relative errors. The database used for training and testing of the proposed ANN and ENN has been collected from the full-scale speed-power trials. Trials are conducted on selected towboats and convoys of barges. The goal of the paper is to show that ENN can be applied on towboat shaft power prediction and can improve the accuracy of the results over the single ANN. Computational results from this numerical example show that ENN definitely outperforms single ANN with two hidden layers. The contribution of this paper is a proposal to use an AIC-based ENN method for predicting towboat shaft powers. The paper is the first one that addresses AIC-based ENN method to predict towboat shaft powers.