<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents an application of artificial neural networks to approximation and identification of additional wave-generated resistance, slamming and internal forces depending on ship motion and wave parameters. The analysis was performed for a typical bulk carrier in ballast loading conditions. The investigations were carried out on the basis of ship response data calculated by means of exact numerical methods. Analytical functions presented in the form of artificial neural networks were analyzed with a view of their accuracy against standard values. Possible ways of application of the artificial neural networks were examined from the point of view of accuracy of approximation and identification of the assumed ship response parameters.</p></abstract>

This paper presents an application of artificial neural networks to approximation and identification of additional wave-generated resistance, slamming and internal forces depending on ship motion and wave parameters. The analysis was performed for a typical bulk carrier in ballast loading conditions. The investigations were carried out on the basis of ship response data calculated by means of exact numerical methods. Analytical functions presented in the form of artificial neural networks were analyzed with a view of their accuracy against standard values. Possible ways of application of the artificial neural networks were examined from the point of view of accuracy of approximation and identification of the assumed ship response parameters.

Application of artificial neural networks to approximation and identification of sea-keeping performance of a bulk carrier in ballast loading condition

Polish Maritime Research

This paper presents an application of artificial neural networks to approximation and identification of additional wave-generated resistance, slamming and...

Application of artificial neural networks to approximation and identification of sea-keeping performance of a bulk carrier in ballast loading condition

Publié en ligne: 25 avr. 2008

Pages: 31 - 39

DOI: https://doi.org/10.2478/v10012-007-0037-6

Mots clés
ship, ship sea keeping qualities, artificial neural networks, slamming, additional wave generated resistance, internal forces, shear forces, bending moments, wave parameters, approximation, identification, assessment

This content is open access.

Application of artificial neural networks to approximation and identification of sea-keeping performance of a bulk carrier in ballast loading condition

Publié en ligne: 25 avr. 2008

Pages: 31 - 39

DOI: https://doi.org/10.2478/v10012-007-0037-6

Mots clésship, ship sea keeping qualities, artificial neural networks, slamming, additional wave generated resistance, internal forces, shear forces, bending moments, wave parameters, approximation, identification, assessment

This content is open access.

Mots clés
ship, ship sea keeping qualities, artificial neural networks, slamming, additional wave generated resistance, internal forces, shear forces, bending moments, wave parameters, approximation, identification, assessment