Study on Using Recursive Neural Networks for System Identification of Ship Dynamics and Maneuverability Prediction
Date Issued
2004
Date
2004
Author(s)
Chang, Tun-Li
DOI
zh-TW
Abstract
In general, ship maneuvering motions are treated as the responses of a time dependent system modeled by nonlinear equations of motions. However, since a few years ago, recursive neural networks technique has been demonstrated applicable for simulating the maneuvers of naval ships as well as that of submarines. Therefore, in order to simulate maneuvering motions and predict maneuverability of a commercial ship, the method of using recursive neural networks modeling may be also available besides the traditional methods such as using hydrodynamic modeling or response modeling.
In this study, a recursive neural network model is developed and applied to simulate the maneuvers of a 192 meter long tanker, which may have inherent poor course stability. In the present model, lateral forces due to rudder angle and centrifugal force, longitudinal forces due to propeller thrust and centrifugal force, as well as Munk moment, used as the inputs of the recursive neural networks, are related to the input control variables such as ruder angle, propeller revolution and the output state variables such as motion velocities by very simplified functions without any undetermined hydrodynamic coefficients or empirical factors. The present recursive neural network is constructed with one input layer, one output layer and two hidden layers. Not only the above-stated forces, but also the outputs of surge velocity, sway velocity and yaw rate are fed back to the input layer of the network. In this study, the existing ship maneuver simulation program, which is developed basing on Japan MMG hydrodynamic model, is used for generating all the sample data of maneuvers for training and validating the recursive neural network.
As a result, although there is still some discrepancy on ship velocity prediction, it is shown that the present recursive neural network model is valid as a tool to simulate maneuvering motions and predict maneuverability for a commercial ship. Furthermore, the least sea trial data need to be obtained for training a recursive neural network and reflecting the maneuverability of a real ship is also discussed in this study.
Subjects
船舶動態
操縱性能
遞迴類神經網路
系統鑑定
ship dynamics
maneuverability
system identification
recursive neural network
SDGs
Type
thesis