Surface Vessel Motion Model Based on RBF Neural Networks
ZHOU Jing
SONG Hui
Abstract：The ship motion model is the critical part of the ship maneuvering simulation system .The method that stress analysis based on hydrodynamic model is one of the most widely used and effective solution .But in the practical application , there are so many interference factors ,nonlinear stress ,and the hydrodynamic derivative problems ,that makes it difficult to keep the balance accuracy and real‐time performance .In order to solve the accuracy of ship motion under the action of a varie‐ty of external and real time ,a kind of radial basis function neural network(RBFN) is presented based on the moving fitting method .Firstly ,a RBFN with the structure of the three layer neural network model is built ,the transfer function of the hid‐den layer and the output layer neurons are set to the Gauss function and linear function respectively ,through the study on ac‐cumulation of historical data to takes the advantage of approximation ability ,classification ability and learning speed of RBFN to calculate the vessel’s movement .The result of simulation shows that the method is good at real‐time performance ,robust‐ness ,precision ,and it has broad application prospects .
Keywords：radical basis function networksship maneuvering simulation systemmotion model
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 1588-1591 )