[1]
Alarcin F.; Gulez K.; Rudder roll stabilisation for fishing vessel using neural network approach, Ocean Engineering, 34 (2007) 1811-1817.
DOI: 10.1016/j.oceaneng.2007.03.001
Google Scholar
[2]
Beale, M.H., Hagan, M.T., Demuth, H.B., Neural Network ToolboxTM. User's Guide, The Mathworks Inc., www. mathworks. com, (2013).
Google Scholar
[3]
Burns R.S.; The use of artificial neural networks for the intelligent optimal control of surface ships, IEEE Journal of Oceanic Engineering, 20 1 (1995) 65-72.
DOI: 10.1109/48.380245
Google Scholar
[4]
Clausen H.B.; Lutzen M.; Friis-Hansen A.; Bjorneboe N.; Baysian and neural networks for preliminary ship design, Marine Technology, 38 4 (2001) 268-277.
DOI: 10.5957/mt1.2001.38.4.268
Google Scholar
[5]
Chiu F-C; Chang T-L; Go J.; Chou S-K; Chen W-C; A recurrent neural network model for ship manoeuvrability prediction, Proceedings of the MTS/IEEE Conference OCEANS'04, Nov 9-12 2004, Kobe, Japan, (2004) 1211-1218.
DOI: 10.1109/oceans.2004.1405752
Google Scholar
[6]
Chitu G.M. and Zăgan R., Comparative study of dynamic nautical features of turning computer assist and sea trial, International Journal of Modern Manufacturing Technologies, I 1 (2009) 21-24.
Google Scholar
[7]
Fossen, T., Fjellstad, O.E., Nonlinear modeling of marine vehicles in 6 degrres of freedom, Journal of Mathematical Modeling of Systems, (1995).
Google Scholar
[8]
Haddara M.R.; Wishahy M.; An investigation of roll characteristics of two full scalke ships at sea, Ocean Engineering, 29 (2002) 651-666.
DOI: 10.1016/s0029-8018(01)00035-x
Google Scholar
[9]
Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities, Proc Nat Acad Sci, 79 (1982) 2554–2558.
DOI: 10.1073/pnas.79.8.2554
Google Scholar
[10]
Im N., Hasegawa K, Automatic ship berthing using parallel neural controllers, Proceedings of the IFAC Conference on Control applications in Marine Systems, 18-20 July, Glasgow, UK, (2001).
Google Scholar
[11]
Moreira L., Soares C.G., Dynamic model of manoeuvrability using recursive neural network, Ocean Engineering, 30 (2003) 1669-1697.
DOI: 10.1016/s0029-8018(02)00147-6
Google Scholar
[12]
Nazari A., Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels, Neural Comput Appl. doi: 10. 1007/s00521-011-0761-9, (2011).
DOI: 10.1007/s00521-011-0761-9
Google Scholar
[13]
Rojas, R., Neural Networks – A systematic introduction, Springer-Verlag, berlin, New-York, (1996).
Google Scholar
[14]
Somkuwar ,V., Prediction of Mechanical Properties of Steel Using Artificial Neural Network, International Journal of Engineering, Business and Enterprise Applications (IJEBEA), 3 1 (2012) 7–13.
Google Scholar
[15]
Somkuwar, V., Prediction of Hardness of High Speed Steel Using Artificial Neural Network, International Journal of Engineering Science and Innovative Technology (IJESIT), 2 2 (2013) 93-98.
Google Scholar
[16]
Unar M.A., Murray-Smith D.J., Automatic Steering of Ships Using Neural Networks, International Journal of adaptive Control and Signal Processing, 13, (1999) 203-218.
DOI: 10.1002/(sici)1099-1115(199906)13:4<203::aid-acs544>3.0.co;2-t
Google Scholar
[17]
Unar M.A., Rajput A.Q.K., Memon Z.A., An intelligent autopilot for a container ship, Proceedings of the First International Conference on Modelling, Simulation and applied Optimization, Sharjah, USA, 1-3 February (2005).
Google Scholar
[18]
Zăgan R., Bormambet M., Zăgan S., Chiţu G.M., Neural networks to predict microhardness of naval steel by chemical composition, International Journal of Modern Manufacturing Technologies V 2 (2013) 103-110.
Google Scholar
[19]
Xu J., Haddara M.R., Estimation of wave induced ship hull bending moment from ship motion measurements, Marine Structures, 14 (2001) 593-610.
DOI: 10.1016/s0951-8339(01)00010-7
Google Scholar
[20]
Yu, H., Wilamowski, B. M., Industrial Electronics Handbook, 2nd Edition, Volume 5, Publisher CRC Press, Pages 12-1 to 12-15, (2011).
Google Scholar