Application of an Artificial Neural Network and Multiple Nonlinear Regression to estimate container ship length between perpendiculars

• Autorzy: Cepowski Tomasz , Chorab Paweł , Łozowicka Dorota

Identyfikatory

DOI

10.2478/pomr-2021-0019

• Języki publikacji: EN

Abstrakty

EN

Container ship length was estimated using artificial neural networks (ANN), as well as a random search based on Multiple Nonlinear Regression (MNLR). Two alternative equations were developed to estimate the length between perpendiculars based on container number and ship velocity using the aforementioned methods and an up-to-date container ship database. These equations could have practical applications during the preliminary design stage of a container ship. The application of heuristic techniques for the development of a MNLR model by variable and function randomisation leads to the automatic discovery of equation sets. It has been shown that an equation elaborated using this method, based on a random search, is more accurate and has a simpler mathematical form than an equation derived using ANN.

Słowa kluczowe

EN

ship design; ANN; regression; container ship; length

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2021
• Tom: nr 2
• Strony: 36--45
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Cepowski Tomasz

• Akademia Morska w Szczecinie, W.Chrobrego 1-2, 70-500 Szczecin, Poland

autor

Chorab Paweł

• Akademia Morska w Szczecinie, W.Chrobrego 1-2, 70-500 Szczecin, Poland

autor

Łozowicka Dorota

• Akademia Morska w Szczecinie, W.Chrobrego 1-2, 70-500 Szczecin, Poland

Bibliografia

• 1. D.G.M. Watson, “Practical Ship Design”, Volume 1. Elsevier Science. 1998.
• 2. K.J. Rawson and E.C. Tupper, Basic Ship Theory: Ship Dynamics and Design. Butterworth-Heinemann. 2001.
• 3. A. Papanikolaou, Ship Design: Methodologies of Preliminary Design. Dordrecht: Springer. 2014.
• 4. J.H. Evans, Basic Design Concepts, Naval Engineers Journal, 1959.
• 5. D.J. Andrews, An Integrated Approach to Ship Synthesis, Trans. RINA. 1985.
• 6. W. Chądzyński, “Elements of contemporary design methods of floating objects”, Scientific Reports of Szczecin University of Technology. (in Polish). 2001.
• 7. G.P. Piko, Regression Analysis of Ship Characteristics, Canberra: C. J. THOMPSON Commonwealth Government Printer. 1980.
• 8. H.O. Kristensen, “Determination of Regression Formulas for Main Dimensions of Container Ships based on IHS Fairplay data”. Project no. 2010-56, Emissionsbeslutning sstøttesystem. Work Package 2, Report no. 03. Technical University of Denmark. 2013.
• 9. Sea-web Ships, IHS Markit, https://maritime.ihs.com, (accessed 8 Decemeber 2020)
• 10. S. Gurgen, I. Altin, I. and O. Murat, “Prediction of main particulars of a chemical tanker at preliminary ship design using artificial neural network”, Ships and Offshore Structures, 13(5), 459-465, 2018.
• 11. A.D. Alkan, K. Gulez and H. Yilmaz, “Design of a robust neural network structure for determining initial stability particulars of fishing vessels”, Ocean Engineering 31 761–777, 2004.
• 12. S. Ekinci, U.B. Celebi, M. Bal, M.F. Amasyali and U.K. Boyaci, “Predictions of oil/chemical tanker main design parameters using computational intelligence techniques”, Applied Soft Computing, 11 2356–2366, 2011.
• 13. T. Abramowski, “Application of Artificial Intelligence Methods to Preliminary Design of Ships and Ship Performance Optimization”, Naval Engineers Journal, 125 (3) 101-112, 2013.
• 14. T. Cepowski, “The prediction of ship added resistance at the preliminary design stage by the use of an artificial neural network”, Ocean Engineering 195, 106657. 2020. doi.org/10.1016/j.oceaneng.2019.106657
• 15. J.G. Song, L.H. Liang, S.T. Zhang and J.M. Wang, “Design and experimental investigation of a GA-based control strategy for a low-speed fin stabilizer”, Ocean Engineering, 2018. 10.1016/j.oceaneng.2020.108234
• 16. V. Sahin and N. Vardar, “Determination of Wastewater Behavior of Large Passenger Ships Based on Their Main Parameters in the Pre-Design Stage”, Journal of Marine Science and Engineering, 8 (8), 2020. DOI: 10.3390/ jmse8080546
• 17. Luan Thanh Le, Gunwoo Lee, Keun-Sik Park and Hwayoung Kim, “Neural network-based fuel consumption estimation for container ships in Korea”, Maritime Policy and Management, 47:5, 615-632, 2020. DOI: 10.1080/03088839.2020.1729437
• 18. X. Cheng, S. Chen and C. Diao, “Simplifying Neural Network Based Model for Ship Motion Prediction: A Comparative Study of Sensitivity Analysis”, Proceeding of the ASME 36th International Conference on Ocean, Offshore and Arctic Engineering, Vol. 1. 2017.
• 19. S. Haykin, “Neural Networks: A Comprehensive Foundation”. New York: Macmillan Publishing. 1994.
• 20. J.H. Lee, T. Delbruck and M. Pfeiffer, “Training Deep Spiking Neural Networks Using Backpropagation”, Frontiers in Neuroscience, 10, 508, 2016.
• 21. L. Fausett, Fundamentals of Neural Networks. New York: Prentice Hall. 1994.
• 22. D. Patterson, Artificial Neural Networks. Singapore: Prentice Hall. 1996.
• 23. C. Bishop, Neural Networks for Pattern Recognition, Oxford: University Press. 1995.
• 24. J. Shepherd, Second-Order Methods for Neural Networks. Springer: New York. 1997.
• 25. J. Adamowski, H.F. Chan, S.O. Prasher, B. Ozga-Zielinski and A. Sliusarieva, “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal”, Water Resources Research, 48W01528, 2012.
• 26. E. Tsykin, “Multiple nonlinear regressions derived with choice of free parameters, Applied Mathematical Modelling”, Volume 8 (4) 288-292, 1984.
• 27. T. Cepowski, ndCurveMaster n-dimensional Curve and Surface Fitting Software, https://www.ndcurvemaster.com, (accessed 13 December 2020).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).