Abstract
A neural network based vessel drift prediction system is proposed. The input vector of the network is formed by ship speed components, the rudder deflection angle, projection of relative wind velocity on the transversal ship axis. A transversal component of the speed (i.e., drift speed) forms the network output. A feedforward neural network with one hidden layer is used as a basic architecture for the drift prediction. Such architecture meets the requirements of the universal approximation theorem. The system takes into account mainly the wind influence for dead reckoning positioning, and the wave influence is not included in the system operation algorithm. The training set is formed by using the MSS toolbox MATLAB software for a container ship for typical motion scenarios with moderate wind velocity. The results of the neural system testing show that the use of the neural network can improve dead reckoning positioning accuracy to a good extent.
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Deryabin, V.V. (2022). A Vessel Drift Prediction System on the Basis of a Neural Network. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). IITI 2021. Lecture Notes in Networks and Systems, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-87178-9_8
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DOI: https://doi.org/10.1007/978-3-030-87178-9_8
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