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Publications
Moreira, L. and Guedes Soares, C. (2005), "Analysis of Recursive Neural Networks Performance Trained with Noisy Manoeuvring Data", Maritime Transportation and Exploitation of Ocean and Coastal Resources, Guedes Soares, C., Garbatov, Y. and Fonseca, N. (Eds.), Taylor & Francis, London, UK, Vol. 1, pp. 733-744
This paper presents a Recursive Neural Network (RNN) manoeuvring simulation model for surface ships. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive output velocities of sway and yaw. This model is used to test the capabilities of artificial neural networks in manoeuvring simulation of ships when the data used for training is noisy. Two manoeuvres are simulated: tactical circles and zigzags. The results between both simulations are compared in order to analyse the accuracy of the Recursive Neural Network and its generalization ability. The simulations are performed for the Mariner hull. The data generated to train the network is obtained from a manoeuvrability model performing the simulation of different manoeuvring tests. The noisy data is generated introducing white noise into two inputs of the system: the rudder angle and advance speed of the ship. The Recursive Neural Network proved to be a robust and accurate tool for manoeuvring simulation.
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