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A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification

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Polish Maritime Research
Special Issue: Coastal, Offshore and Ocean Engineering

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1. Chai, Y., L. Jia, Z. Zhang: Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application.Search in Google Scholar

2. Cpałka, K.: Design of Interpretable Fuzzy Systems, Springer, 2017.10.1007/978-3-319-52881-6Search in Google Scholar

3. Cpałka, K., L. Rutkowski: On Designing of Flexible Neuro-Fuzzy Systems for Classification.Search in Google Scholar

4. Driankov, D., H. Hellendoorn, M. Reinfrank: An Introduction to Fuzzy Control, Springer Berlin Heidelberg, 1996.10.1007/978-3-662-03284-8Open DOISearch in Google Scholar

5. Goerlandt, F., J. Montewka: Maritime transportation risk analysis: Review and analysis in light of some foundational issues, Reliab. Eng. Syst. Saf. 138 (2015), pp. 115–134.10.1016/j.ress.2015.01.025Search in Google Scholar

6. Hansen, M.G., T.K. Jensen, F. Ennemark: Empirical Ship Domain based on AIS Data, (2013), pp. 931–940.10.1017/S0373463313000489Search in Google Scholar

7. van Iperen, E.: Classifying ship encounters to monitor traffic safety on the North Sea from AIS data, TransNav - Int. J. Mar. Navig. Saf. Sea Transp. 9 (2015), pp. 53–60.10.12716/1001.09.01.06Search in Google Scholar

8. Lazarowska, A.: Multi-criteria ACO-based Algorithm for Ship’s Trajectory Planning, TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 11 (2017), pp. 31–36.10.12716/1001.11.01.02Search in Google Scholar

9. Lisowski, J.: Game control methods in avoidance of ships collisions, Polish Marit. Res. 19 (2012), pp. 3–10.10.2478/v10012-012-0016-4Search in Google Scholar

10. Lisowski, J., A. Lazarowska: The radar data transmission to computer support system of ship safety, Solid State Phenom. 196 (2013), pp. 95–101.10.4028/www.scientific.net/SSP.196.95Search in Google Scholar

11. Nowicki, R.K.: Fuzzy decision systems in issues of limited knowledge (in Polish), Akademia Oficyna Wydawnicza EXIT, 2009.Search in Google Scholar

12. Pietrzykowski, Z., P. Wo, P. Borkowski: Decision Support in Collision Situations at Sea, (2017), pp. 447–464.10.1017/S0373463316000746Search in Google Scholar

13. Rutkowska, D.: Neuro-Fuzzy Architectures and Hybrid Learning, Physica-Verlag HD, Heidelberg, 2002.10.1007/978-3-7908-1802-4Search in Google Scholar

14. Rutkowska, D., R. Nowicki: Implication-Based Neuro-Fuzzy Architectures, Int. J. Appl. Math. Comput. Sci. 10 (2000), pp. 675–701.Search in Google Scholar

15. Rutkowski, L., K. Cpalka: Flexible neuro-fuzzy systems, IEEE Trans. Neural Networks. 14 (2003), pp. 554–574.10.1109/TNN.2003.81169818238039Open DOISearch in Google Scholar

16. Szlapczynski, R.: A new method of planning collision avoidance manoeuvres for multi-target encounter situations, J. Navig. 61 (2008).10.1017/S0373463307004638Search in Google Scholar

17. Szlapczynski, R., J. Szlapczynska: Customized crossover in evolutionary sets of safe ship trajectories, Int. J. Appl. Math. Comput. Sci. 22 (2012).10.2478/v10006-012-0074-xSearch in Google Scholar

18. Szłapczynska, J.: Multi-objective Weather Routing with Customised Criteria and Constraints, J. Navig. 68 (2015), pp. 338–354.10.1017/S0373463314000691Search in Google Scholar

19. Szłapczyński, R., R. Smierzchalski: Supporting navigator’s decisions by visualizing ship collision risk, Polish Marit. Res. 16 (2009).10.2478/v10012-008-0015-7Search in Google Scholar

20. Wang, Y., H. Chin: An Empirically-Calibrated Ship Domain as a Safety Criterion for Navigation in Confined Waters, (2015).10.1017/S0373463315000533Search in Google Scholar

21. Van Westrenen, F., J. Ellerbroek: The Effect of Traffic Complexity on the Development of Near Misses on the North Sea, IEEE Trans. Syst. Man, Cybern. Syst. 47 (2017), pp. 432–440.10.1109/TSMC.2015.2503605Open DOISearch in Google Scholar

22. Wu, X., A.L. Mehta, V.A. Zaloom, B.N. Craig: Analysis of waterway transportation in Southeast Texas waterway based on AIS data, Ocean Eng. 121 (2016), pp. 196–209.10.1016/j.oceaneng.2016.05.012Search in Google Scholar

23. Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning-I, (1975), pp. 199–249.10.1016/0020-0255(75)90036-5Search in Google Scholar

24. Zhang, W., F. Goerlandt, P. Kujala, Y. Wang: An advanced method for detecting possible near miss ship collisions from AIS data, Ocean Eng. 124 (2016), pp. 141–156.10.1016/j.oceaneng.2016.07.059Search in Google Scholar

25. A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems, Int. J. Approx. Reason. 52 (2011) pp. 894–913.10.1016/j.ijar.2011.03.004Search in Google Scholar

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Language:
English
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Journal Subjects:
Engineering, Introductions and Overviews, other, Geosciences, Atmospheric Science and Climatology, Life Sciences