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The Assessment of Ship Drift on the Basis of a Neural Network

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Abstract

A neural network based vessel drift predicting system has been developed. The network takes as input signals velocity components values, a rudder deflection angle and the transversal projection of relative wind velocity. These values are taken at the bounds of a time prediction interval. A value of drift speed at the right bound of the interval is the network output. A usual fully-connected perceptron-type neural network is used for the prediction problem solution. The training data are collected by imitation modeling for a containership. Marine Systems Simulator (MSS) MATLAB based toolbox is used as the software for the purpose. The process of the data collection includes repeating prevention and classification stages. The first stage ensures that closely placed input samples are not to be used. The second stage ensures the uniformity of samples distribution in the working space of the input signal. The training data are simulated to be collected during usual operation of the ship, when the ship is track controlled. The result of the test of the proposed neural scheme shows that the network may either improve dead reckoning accuracy or decrease it, as well.

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Authors and Affiliations

  1. Department of Navigation, Admiral Makarov State University of Maritime and Inland Shipping, 198035, St. Petersburg, Russia

    V. V. Deryabin

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  1. V. V. Deryabin
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Correspondence to V. V. Deryabin.

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Deryabin, V.V. The Assessment of Ship Drift on the Basis of a Neural Network. Aut. Control Comp. Sci. 56, 447–454 (2022). https://doi.org/10.3103/S0146411622050030

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