Title :
Ship motion prediction by radial basis neural networks
Author :
De Masi, Giulia ; Gaggiotti, Federico ; Bruschi, Roberto ; Venturi, Marco
Author_Institution :
Saipem Energy Services, Fano, Italy
Abstract :
A radial basis function (RBF) artificial neural network (ANN) is proposed to develop a model of short term (50 seconds) prediction of vessel heave motion. This is a cutting edge topic in Ocean Engineering, since it is primary to support marine operations of vessels in harsh sea environment. The present study proposes a combined application of ANN and Hilbert transform. The time series of vessel heave motions, measured by on board Inertial Platform System, are used to train the network and to find the best configuration. The results indicate that RBF networks provide an effective and accurate tool to predict vessel motions produced by waves.
Keywords :
Hilbert transforms; hydrodynamics; inertial navigation; learning (artificial intelligence); marine engineering; mechanical engineering computing; ocean waves; radial basis function networks; ships; time series; Hilbert transform; RBF networks; artificial neural network training; harsh sea environment; inertial platform system; marine operations; ocean engineering; ocean waves; radial basis function; ship motion prediction; time series; vessel heave motion; Artificial neural networks; Forecasting; Oceans; Predictive models; Radial basis function networks; Sea measurements; Time series analysis; nowcasting; radial basis function neural network (RBFNN); vessel motion forecasting;
Conference_Titel :
Hybrid Intelligent Models And Applications (HIMA), 2011 IEEE Workshop On
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9907-6
DOI :
10.1109/HIMA.2011.5953967
