We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Artificial neural network for predicting values of residuary resistance per unit weight of displacement.
- Authors
Šegota, Sandi Baressi; Anđelić, Nikola; Kudláček, Jan; Čep, Robert
- Abstract
This paper proposes the usage of an Artificial neural network (ANN) to predict the values of the residuary resistance per unit weight of displacement from the variables describing ship’s dimensions. For this purpose, a Multilayer perceptron (MLP) regressor ANN is used, with the grid search technique being applied to determine the appropriate properties of the model. After the model training, its quality is determined using R² value and a Bland-Altman (BA) graph which shows a majority of values predicted falling within the 95% confidence interval. The best model has four hidden layers with ten, twenty, twenty and ten nodes respectively, uses a relu activation function with a constant learning rate of 0.01 and the regularization parameter L2 value of 0.001. The achieved model shows a high regression quality, lacking precision in the higher value range due to the lack of data.
- Subjects
REGULARIZATION parameter; ARTIFICIAL neural networks; MULTILAYER perceptrons; MODEL railroads; CONFIDENCE intervals
- Publication
Annals of Maritime Studies / Pomorski Zbornik, 2019, Vol 57, Issue 1, p9
- ISSN
0554-6397
- Publication type
Article
- DOI
10.18048/2019.57.01.