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2012 | Vol. 6, no. 4 | 453--458
Tytuł artykułu

Neuroevolutionary Ship Handling System in a Windy Environment

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Języki publikacji
EN
Abstrakty
EN
This paper presents the advanced intelligent ship handling system able to simulate and demonstrate learning behavior of artificial helmsman which controls model of ship in a windy environment of restricted water area. Simulated helmsmen are treated as individuals in population, which through environmental sensing and evolutionary algorithms learns to perform given task efficiently. The task is: safe navigation through heavy wind channels. Neuroevolutionary algorithms, which develop artificial neural networks through evolutionary operations, have been applied in this system.
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Rocznik
Strony
453--458
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
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  • [8] Kenneth O. S., Bryant B. D. and Risto M. (2005). "Real-time neuroevolution in the NERO video game." IEEE Transactions on Evolutionary Computation 9(6): 653-668.
  • [9] Kenneth O. S. and Risto M. (2002a). Efficient evolution of neural network topologies. Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02, IEEE Computer Society: 1757-1762.
  • [10] Kenneth O. S. and Risto M. (2002b). Efficient Reinforcement Learning Through Evolving Neural Network Topologies. Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers Inc.: 569-577.
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  • [13] Łącki M. (2008). Neuroevolutionary approach towards ship handling. TST, Katowice-Ustroń, WKŁ.
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  • [15] Łącki M. (2010b). "Wyznaczanie punktów trasy w neuroewolucyjnym sterowaniu statkiem." Logistyka 6.
  • [16] Łącki M. (2010c) Speciation of Population in Neuroevolutionary Ship Handling. TransNav - International Journal on Marine Navigation and Safety of Sea Transportation, 4(2), 211-216
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  • [18] Siebel N. T. and Sommer G. (2007). "Evolutionary reinforcement learning of artificial neural networks." International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems 4(3): 171-183.
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  • [20] Tesauro G. (1995). "Temporal difference learning and TD-Gammon." Communications of the ACM 38(3): 58-68
Typ dokumentu
Bibliografia
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