HomePage
 




 


 

ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@am.gdynia.pl
Neuroevolutionary Ship Handling System in a Windy Environment
1 Gdynia Maritime University, Gdynia, Poland
ABSTRACT: 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.
REFERENCES
Braun H. and Weisbrod J. (1993). Evolving Feedforward Neural Networks. International Conference on Artificial Neural Networks and Genetic Algorithms, Innsbruck, Springer-Verlag.
De Nardi R., Togelius J., Holland O. E. and Lucas S. M. (2006). Evolution of Neural Networks for Helicopter Control: Why Modularity Matters. Evolutionary Computation, 2006. CEC 2006. IEEE Congress on: 1799-1806.
Filipowicz W., Łącki M. and Szłapczyńska J. (2006). Multicriteria Decision Support for Vessels Routing. Archives of Transport 17: 71-83.
Fossen T., I. (2011). Handbook of marine craft hydrodynamics and motion control, John Wiley & Sons, Ltd.
Janghel R. R., Tiwari R., Kala R. and Shukla A. (2012). Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution. IJISSC 3(1): 1-9.
Kaelbling L. P., Littman M. L. and Moore A. W. (1996). Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research cs.AI/9605: 237-285.
Kappatos V. A., Georgoulas G., Stylios C. D. and Dermatas E. S. (2009). Evolutionary dimensionality reduction for crack localization in ship structures using a hybrid computational intelligent approach. Proceedings of the 2009 international joint conference on Neural Networks. Atlanta, Georgia, USA, IEEE Press: 1907-1914.
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.
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.
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.
Larkin D., Kinane A. and O'Connor N. (2006). Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices. Proceedings of the 13th international conference on Neural information processing - Volume Part III. Hong Kong, China, Springer-Verlag: 1178-1188.
Łącki M. (2007). Machine Learning Algorithms in Decision Making Support in Ship Handling. TST, Katowice-Ustroń, WKŁ.
Łącki M. (2008). Neuroevolutionary approach towards ship handling. TST, Katowice-Ustroń, WKŁ.
Łącki M. (2010a). Model środowiska wieloagentowego w neuroewolucyjnym sterowaniu statkiem. Zeszty Naukowe Akademii Morskiej w Gdyni 67: 31-37.
Łącki M. (2010b). Wyznaczanie punktów trasy w neuroewolucyjnym sterowaniu statkiem. Logistyka 6.
Ratuszniak P. (2012). Processor array design with the use a genetic algorithm. Lecture Notes in Computer Science 7116.
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.
Sutton R. and Barto A. (1998). Reinforcement Learning: An Introduction, The MIT Press.
Tesauro G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM 38(3): 58-68
Citation note:
Łącki M.: Neuroevolutionary Ship Handling System in a Windy Environment. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 6, No. 4, pp. 453-458, 2012

Other publications of authors:


File downloaded 836 times








Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony