Comparison of Fuzzy System with Neural Aggregation FSNA with classical TSK fuzzy system in anti-collision problem of USV

• Autorzy: Szymak P.
• Języki publikacji: EN

Abstrakty

EN

The paper presents the research whose the main goal was to compare a new Fuzzy System with Neural Aggregation of fuzzy rules FSNA with a classical Takagi-Sugeno-Kanga TSK fuzzy system in an anti-collision problem of Unmanned Surface Vehicle USV. Both systems the FSNA and the TSK were learned by means of Cooperative Co-evolutionary Genetic Algorithm with Indirect Neural Encoding CCGA-INE. The paper includes an introduction to the subject, a description of the new FSNA and the tuning method CCGA-INE, and at the end, numerical research results with a summary. The research includes comparison of the FSNA with the classical TSK system in the anti-collision problem of the USV.

Słowa kluczowe

EN

neuro-fuzzy system; neural aggregation of fuzzy rules; cooperative co-evolution; anti-collision of USV

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: nr 3
• Strony: 3--14
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Szymak P.

• Polish Naval Academy Smidowicza 69, 81-127 Gdynia Poland

Bibliografia

• 1. D. Driankov, H. Hellendoorn, M. Reinfrank, An Introduction to Fuzzy Control, Springer-Verlag, 1996.
• 2. T.J. Fossen, Guidance and Control of Ocean Vehicles, John Wiley and Sons Ltd., 1994.
• 3. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, Massachusetts, 1989.
• 4. K. Guney, N. Sarikaya, Comparison of Mamdani and Sugeno Fuzzy Inference System Models for Resonant Frequency Calculation of Rectangular Microstrip Antennas, Progress In Electromagnetics Research B, Vol. 12, p. 81–104, 2009.
• 5. C. Hwang, “The integrated design of fuzzy collisionavoidance and H∞-autopilots on ships”, The Journal of Navigation, Vol. 55(1), pp.117-136, 2002.
• 6. Z. Kitowski, “Autonomous unmanned surface vehicle Edredon”, Polish Hyperbaric Research, Vol. 3(40), 2012, s. 7-22.
• 7. J. Lisowski, “Sensitivity of Computer Support Game Algorithms of Safe Ship Control”, International Journal of Applied Mathematics and Computer Science, Vol. 23, No. 2, 439–446, 2013.
• 8. E. H. Mamdani, S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, International Journal of Man-machine Studies, Vol. 7, p. 1-13, 1975.
• 9. K. Naus, M. Wąż, A simplified navigational chart pyramid dedicated to an autonomous navigational system, Polish Hyperbaric Research, Vol. 3(40), pp. 99-118, 2012.
• 10. S. Osowski, Neural networks for data processing, in polish, Publishing House of Technology University in Warsaw, 2006.
• 11. M. A. Potter, K. A. De Jong, “Cooperative coevolution: An architecture for evolving coadapted subcomponents”, Evolutionary Computation, Vol. 8(1), p. 1–29, 2000.
• 12. T. Praczyk, “Neural anti-collision system for Autonomous Surface Vehicle”, Neurocomputing, Vol. 149, Part B, p. 559–572, 2015.
• 13. T. Praczyk, P. Szymak, “Decision System for a Team of Autonomous Underwater Vehicles – Preliminary Report”, Neurocomputing, Vol. 74 (17), pp. 3323-3334, 2011.
• 14. T. Praczyk, P. Szymak, “Using Genetic Algorithms to Fix a Route for an Unmanned Surface Vehicle”, in Proceedings of the 17th International Conference on Methods and Models in Automation and Robotics, pp. 487-492, 2012.
• 15. P. Szymak, T. Praczyk, “Using Neural-EvolutionaryFuzzy Algorithm for Anti-collision System of Unmanned Surface Vehicle”, in Proceedings of the 17th International Conference on Methods and Models in Automation and Robotics, pp. 286-290, 2012.
• 16. P. Szymak, “Course Control of Unmanned Surface Vehicle”, Solid State Phenomena, Vol. 196, pp. 117-123, 2013.
• 17. T. Takagi, M. Sugeno, “Fuzzy Identification of Systems and its Application to Modelling and Control”, IEEE Transactions on Systems, Man and Cybernetics, vol. 15, pp. 116-132, 1985.
• 18. J. Vieira, F.M. Dias, A. Mota, Neuro-Fuzzy Systems: A Survey, WSEAS Transactions on Systems, 3(2), 2004.
• 19. L. Zadeh, “Fuzzy sets”, Information and Control, vol. 8, pp. 338–353, 1965.
• 20. Y. Zhuo, “An intelligent decision support system to ship anti-collision in multi-ship encounter”, in Proceedings of the Intelligent Control and Automation 2008, pp. 1066–1071, 2008.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)