Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

M Míguez González; F. López Peña; V. Díaz Casás; Roberto Galeazzi; Mogens Blanke

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

M Míguez González
, F. López Peña
, V. Díaz Casás
, Roberto Galeazzi
, Mogens Blanke

University of A Coruna

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network is shown to be capable of forecasting the ship’s roll motion in realistic scenarios.

Original language	English
Title of host publication	Proceedings of the Twenty-first International Offshore and Polar Engineering Conference
Publication date	2011
Pages	522-529
ISBN (Print)	978-1-880653-96-8
Publication status	Published - 2011
Event	21st International Offshore and Polar Engineering Conference - Maui, Hawaii, United States Duration: 19 Jun 2011 → 24 Jun 2011 http://www.isope.org/conferences/conferences.htm

Conference

Conference	21st International Offshore and Polar Engineering Conference
Country/Territory	United States
City	Maui, Hawaii
Period	19/06/2011 → 24/06/2011
Internet address	http://www.isope.org/conferences/conferences.htm

Keywords

Fishing vessels
Parametric roll
Artificial neural networks
Prediction systems

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title = "Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network",

abstract = "Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network is shown to be capable of forecasting the ship{\textquoteright}s roll motion in realistic scenarios.",

keywords = "Fishing vessels, Parametric roll, Artificial neural networks, Prediction systems",

author = "\{M{\'i}guez Gonz{\'a}lez\}, M and \{L{\'o}pez Pe{\~n}a\}, F. and \{D{\'i}az Cas{\'a}s\}, V. and Roberto Galeazzi and Mogens Blanke",

year = "2011",

language = "English",

isbn = "978-1-880653-96-8",

pages = "522--529",

booktitle = "Proceedings of the Twenty-first International Offshore and Polar Engineering Conference",

note = "21st International Offshore and Polar Engineering Conference, ISOPE-2011 ; Conference date: 19-06-2011 Through 24-06-2011",

url = "http://www.isope.org/conferences/conferences.htm",

}

Míguez González, M, López Peña, F, Díaz Casás, V, Galeazzi, R & Blanke, M 2011, Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network. in Proceedings of the Twenty-first International Offshore and Polar Engineering Conference. pp. 522-529, 21st International Offshore and Polar Engineering Conference, Maui, Hawaii, United States, 19/06/2011. <http://www.isope.org>

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network. / Míguez González, M; López Peña, F.; Díaz Casás, V. et al.
Proceedings of the Twenty-first International Offshore and Polar Engineering Conference. 2011. p. 522-529.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

AU - Míguez González, M

AU - López Peña, F.

AU - Díaz Casás, V.

AU - Galeazzi, Roberto

AU - Blanke, Mogens

PY - 2011

Y1 - 2011

N2 - Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network is shown to be capable of forecasting the ship’s roll motion in realistic scenarios.

AB - Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network is shown to be capable of forecasting the ship’s roll motion in realistic scenarios.

KW - Fishing vessels

KW - Parametric roll

KW - Artificial neural networks

KW - Prediction systems

M3 - Article in proceedings

SN - 978-1-880653-96-8

SP - 522

EP - 529

BT - Proceedings of the Twenty-first International Offshore and Polar Engineering Conference

T2 - 21st International Offshore and Polar Engineering Conference

Y2 - 19 June 2011 through 24 June 2011

ER -

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

Abstract

Conference

Keywords

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