Neural Networks Based Prediction Model for Vessel Track Control

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The problem of neural networks implementation for the construction of a predictive model for vessel track control was studied. It has been shown that the vessel track control problem may be considered as an approximation task, and neural networks may be implemented as universal approximating tools. The general structure of the prediction model, based on neural networks, has been developed. The model consists of several two-layered feedforward neural networks, which architectures satisfy the conditions of universal approximation properties. The analysis of the functions of the different neural networks in the prediction model has been performed. The network predicting WGS-84 geodetic latitude as a part of the predictive model has been constructed, trained and validated by using MATLAB software. The validation results show the good prediction precision of the net.

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  1. 1.

    For clarity, let suppose that the route line is a number of waypoints on the Earth ellipsoid, connected with the segments of geodetic lines.


  1. 1

    Chen, S., et al., Approximating explicit model predictive control using constrained neural networks, Proc. of the American Control Conf., Milwauke, 2018, pp. 1520–1527.

  2. 2

    Cybenko, G., Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst., 1989, vol. 2, pp. 303–314.

  3. 3

    Haykin, S., Neural Networks and Learning Machines, New York: Prentice Hall, 2009.

  4. 4

    Kainen, P.C., Kurkova, V., and Sanguineti, M., Approximating multivariable functions by feedforward neural nets, in Handbook on Neural Information Processing, New York: Springer, 2013, ch. 5.

  5. 5

    Leshno, M., et al., Multilayer feedforward networks with a nonpolynomial activation function can approximate any function, Neural Networks, 1993, vol. 6, pp. 861–867.

  6. 6

    Lawrynczuk, M., Training of neural models for predictive control, Neurocomputing, 2010, vol. 73, pp. 1332–1343.

  7. 7

    Patan, K., Two stage neural network modeling for robust model predictive control, ISA Trans., 2018, pp. 56–65.

  8. 8

    Ranković, V., et al., Neural network model predictive control of nonlinear systems using genetic algorithms, Int. J. Comput. Commun., 2012, vol. 7, no. 3, pp. 540–549.

  9. 9

    Vincent, A.A. and Hassapis, G., Adaptive predictive control using recurrent neural network identification, Proc. of 17th Mediterranean Conf. on Control & Automotion, Thessaloniki, 2009, pp. 61–66.

  10. 10

    Soloway, D. and Pamela, J.H., Neural generalized predictive control. A Newton-Raphson implementation, Proceedings of the 1996 IEEE International Symp. on Intelligent Control, Dearborn, 1996, pp. 277–282.

  11. 11

    Reese, B. and Collins, E., A graph search and neural network approach to adaptive nonlinear model predictive control, Eng. Appl. Artif. Intell., 2016, vol. 55, pp. 250–268.

  12. 12

    Yan, Z. and Wang, J., Model predictive control for tracking of underactuated vessels based on recurrent neural networks, IEEE J. Oceanic Eng., 2012, vol. 37, no. 4, pp. 717–726.

  13. 13

    Fossen, T.I., Handbook of Marine Craft Hydrodynamics and Motion Control, Chichester: John Wiley & Sons, 2011.

  14. 14

    Faltinsen, O.M., Sea Loads on Ships and Offshore Structures, Cambridge: Cambridge University Press, 1990.

  15. 15

    Nguyen, D. and Widrow, B., Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, Proc. of 1990 IJCNN International Joint Conference on Neural Networks, San Diego, 1990, vol. 3, pp. 21–26.

  16. 16

    Foresee, F.D. and Hagan, M.T., Gauss-Newton approximation to Bayesian learning, Proc. of International Conference on Neural Networks (ICNN’97), Houston, 1997, vol. 3, pp. 1930–1935.

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Correspondence to V. V. Deryabin.

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Deryabin, V.V. Neural Networks Based Prediction Model for Vessel Track Control. Aut. Control Comp. Sci. 53, 502–510 (2019) doi:10.3103/S0146411619060038

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  • vessel
  • track control
  • neural network