Neural network based algorithm for ship safe motion control in fuzzy environment
Keywords:
ship trajectory, fuzzy set theory, decision-making, neural networkAbstract
This paper addresses the problem of planning the ship trajectory in collision situations. The ship maneuverability parameters and subjectivity navigator in decisionmaking are taken into account in the proposed process model. The main objective of the study is to propose the designation of safe ship trajectory in collision situations as a multistage decision-making process in the fuzzy environment. The specific objective is to use a method based on neural network to determine the best ship trajectory in collision situations.References
Bellman, R.E., Zadeh, L.A., 1970, Decision Making in a Fuzzy Environment, Management Science, no. 17.
2. Francelin, R.A., Gomide, F.A.C., 1993, A Neural Network for Fuzzy Decision Making Problems, Proc. of 2nd IEEE International Conference on Fuzzy Systems, vol. 1, s. 655–660.
3. Francelin, R., Kacprzyk, J., Gomide, F., 2001, Neural Network Based Algorithm for Dynamic System Optimization, Asian Journal of Control, vol. 3, no. 2, s. 131–142.
4. Hiraga, I., Furuhashi, T., Uchikawa, Y., Nakayama, S., 1995, An Acquisition of Operator's Rules for Collision Avoidance Using Fuzzy Neural Networks, IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, s. 280–287.
5. Lisowski, J., 2012, The Optimal and Safe Ship Trajectories for Different Forms of Neural State Constraints, Solid State Phenomena, vol. 180.
6. Mohamed-Seghir, M., 2012, The Branch-and-bound Method and Genetic Algorithm in Avoidance of Ships Collisions in Fuzzy Environment, Polish Maritime Research 19, Special Issue, s. 45–49.
7. Pietrzykowski, Z., Magaj, J., Wolejsza, P., Chomski, J., 2010, Fuzzy Logic in the Navigational Decision Support Process Onboard a Sea-Going Vessel, Artificial Intelligence and Soft Computing, Springer, Berlin – Heidelberg, s. 185–193.
8. Rocha, A.F., 1992, Neural Nets: a Theory of Brain an Machines, vol. 638, Springer-Verlag, Berlin – Heidelberg – New York.
Remove 1. Bellman, R.E., Zadeh, L.A., 1970, Decision Making in a Fuzzy Environment, Management Science, no. 17.
2. Francelin, R.A., Gomide, F.A.C., 1993, A Neural Network for Fuzzy Decision Making Problems, Proc. of 2nd IEEE International Conference on Fuzzy Systems, vol. 1, s. 655–660.
3. Francelin, R., Kacprzyk, J., Gomide, F., 2001, Neural Network Based Algorithm for Dynamic System Optimization, Asian Journal of Control, vol. 3, no. 2, s. 131–142.
4. Hiraga, I., Furuhashi, T., Uchikawa, Y., Nakayama, S., 1995, An Acquisition of Operator's Rules for Collision Avoidance Using Fuzzy Neural Networks, IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, s. 280–287.
5. Lisowski, J., 2012, The Optimal and Safe Ship Trajectories for Different Forms of Neural State Constraints, Solid State Phenomena, vol. 180.
6. Mohamed-Seghir, M., 2012, The Branch-and-bound Method and Genetic Algorithm in Avoidance of Ships Collisions in Fuzzy Environment, Polish Maritime Research 19, Special Issue, s. 45–49.
7. Pietrzykowski, Z., Magaj, J., Wolejsza, P., Chomski, J., 2010, Fuzzy Logic in the Navigational Decision Support Process Onboard a Sea-Going Vessel, Artificial Intelligence and Soft Computing, Springer, Berlin – Heidelberg, s. 185–193.
8. Rocha, A.F., 1992, Neural Nets: a Theory of Brain an Machines, vol. 638, Springer-Verlag, Berlin – Heidelberg – New York.
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