Artificial neural network for predicting values of residuary resistance per unit weight of displacement

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F19%3A00345307" target="_blank" >RIV/68407700:21220/19:00345307 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.18048/2019.57.01" target="_blank" >https://doi.org/10.18048/2019.57.01</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18048/2019.57.01" target="_blank" >10.18048/2019.57.01</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial neural network for predicting values of residuary resistance per unit weight of displacement

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Artificial neural network for predicting values of residuary resistance per unit weight of displacement

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Maritime & Transportation Sciences

ISSN

0554-6397

e-ISSN

—

Svazek periodika

57

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

HR - Chorvatská republika

Počet stran výsledku

14

Strana od-do

9-22

Kód UT WoS článku

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EID výsledku v databázi Scopus

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