RECURSIVE NEURAL NETWORK MODEL OF CATAMARAN MANOEUVRING

2021 ◽  
Vol 154 (A3) ◽  
Author(s):  
L Moreira ◽  
C Guedes Soares
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Simulation Model ◽  
Network Model ◽  
Neural Network Model ◽  
Full Scale ◽  
Passenger Transport ◽  
Recursive Neural Network ◽  
Rudder Angle ◽  
Scale Data

A neural network model to simulate catamaran manoeuvres is proposed as an alternative to the traditional methodology of developing manoeuvring mathematical models. Data obtained in full-scale trials with a real ship are used to train the model. By recording full-scale trials of catamaran manoeuvres it is possible to generate a neural network model which will allow the prediction of the catamaran manoeuvring performance under different conditions. A Recursive Neural Network (RNN) manoeuvring simulation model is proposed and applied to a catamaran in this specific case. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. Two types of manoeuvres are simulated: tactical circles and zigzags. The results between the full-scale data and the simulations are compared in order to analyze and determine the accuracy of the RNN. The study is performed for a catamaran operating in the Tagus estuary for passenger transport to and from Lisbon.

Recursive Neural Network Model Of Catamaran Manoeuvring

2012 ◽  
Vol 154 (A3) ◽  
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Simulation Model ◽  
Network Model ◽  
Neural Network Model ◽  
Full Scale ◽  
Passenger Transport ◽  
Recursive Neural Network ◽  
Rudder Angle ◽  
Scale Data

A neural network model to simulate catamaran manoeuvres is proposed as an alternative to the traditional methodology of developing manoeuvring mathematical models. Data obtained in full-scale trials with a real ship are used to train the model. By recording full-scale trials of catamaran manoeuvres it is possible to generate a neural network model which will allow the prediction of the catamaran manoeuvring performance under different conditions. A Recursive Neural Network (RNN) manoeuvring simulation model is proposed and applied to a catamaran in this specific case. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. Two types of manoeuvres are simulated: tactical circles and zigzags. The results between the full-scale data and the simulations are compared in order to analyze and determine the accuracy of the RNN. The study is performed for a catamaran operating in the Tagus estuary for passenger transport to and from Lisbon.

Recursive neural network model for analysis and forecast of PM10 and PM2.5

2017 ◽  
Vol 8 (4) ◽  
pp. 652-659 ◽  
Author(s):  
Fabio Biancofiore ◽  
Marcella Busilacchio ◽  
Marco Verdecchia ◽  
Barbara Tomassetti ◽  
Eleonora Aruffo ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Pm10 And Pm2.5 ◽  
Recursive Neural Network

Ionic liquids: prediction of their melting points by a recursive neural network model

2008 ◽  
Vol 10 (3) ◽  
pp. 306 ◽  
Author(s):  
Riccardo Bini ◽  
Cinzia Chiappe ◽  
Celia Duce ◽  
Alessio Micheli ◽  
Roberto Solaro ◽  
...  
Keyword(s):  
Neural Network ◽  
Ionic Liquids ◽  
Network Model ◽  
Neural Network Model ◽  
Melting Points ◽  
Recursive Neural Network

Tacking Simulation of Sailing Yachts - Numerical Integration of Equations of Motion and Application of Neural Network Technique

1995 ◽  
Author(s):  
Yutaka Masayuma ◽  
Toichi Fukasawa ◽  
Hiroshi Sasagawa
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Equations Of Motion ◽  
Back Propagation ◽  
Full Scale ◽  
Hydrodynamic Derivatives ◽  
Neural Network System ◽  
Long Time

The question, "What is the best tacking procedure?", has challenged sailors for a long time. In order to answer this question, it might be best to clarify the mechanism of tacking motions of sailing boats theoretically. However, tacking is a complicated maneuvering motion. Many factors affect tacking, including maneuverability, rolling characteristics, sail performance, etc. The first step to tackle this problem, therefore, is to make a proper model which represents tacking motions. In the present paper, two models are proposed to represent the tacking motions of sail boats. One is a mathematical model and another is a neural network model. In the mathematical model, the tacking motion of the boat is described by partial differential equations with the coordinate system using the horizontal body axes. The hydrodynamic derivatives of the equations are mainly given by model test results and/or full scale measurements. Such coefficients as added mass and added moment of inertia are calculated using the strip method. These equations are integrated using an integration method. In the neural network model, on the other hand, the tacking motion of the boat is regarded as a neural network system consisting of several layers. In the present paper, two hidden layers are used besides the input and output layers. The network constants are tuned using back-propagation procedure. If the rudder angle is inputted, the boat motion can be obtained step by step using the network. Comparing the results obtained by these two methods with the full-scale experiments of a sailing boat, the merit and the demerit of these models are discussed.

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