References
- Alkan A, Gulez K, Yilmaz H. 2004. Design of a robust neural network structure for determining initial stability particulars of fishing vessels. Ocean Eng. 31:761–777.
- Clausen HB, Lützen M, Friis-Hansen A, Bjørneboe N. 2001. Bayesian and neural networks for preliminary ship design. Mar Technol. 38:268–277.
- Cui H, Turan O, Sayer P. 2012. Learning-based ship design optimization approach. Comput-Aided Des. 44:186–195.
- Evans JH. 1959. Basic design concepts. J Am Soc Nav Eng. 71:671–678.
- Gougoulidis G. 2008. The utilization of artificial neural networks in marine applications: an overview. Nav Eng J. 120:19–26.
- Haykin S. 1994. Neural network: a comprehensive foundation. New York (NY): Macmillan College.
- Helvacioglu S, Insel M. 2008. Expert system applications in marine technologies. Ocean Eng. 35:1067–1074.
- Jain P, Deo M. 2006. Neural networks in ocean engineering. Ships Offshore Struct. 1:25–35.
- Kim S-Y, Moon B-Y, Kim D-E. 2004. Optimum design of ship design system using neural network method in initial design of hull plate. KSME Int J. 18:1923–1931.
- Lee A, Kim SE, Suh K-D. 2016. An easy way to use artificial neural network model for calculating stability number of rock armors. Ocean Eng. 127:349–356.
- Lee KH, Kim KS, Lee JH, Park JH, Kim DG, Kim DS. 2007. Development of enhanced data mining system to approximate empirical formula for ship design. In: Zhang Z, Siekmann J, editors. Proceedings of the 2nd International Conference on Knowledge Science, Engineering and Management, KSEM 2007; November 28–30; Melbourne, Australia. Berlin, Heidelberg: Springer. p. 425–436.
- López M, Iglesias G. 2013. Artificial intelligence for estimating infragravity energy in a harbour. Ocean Eng. 57:56–63.
- Mason A, Couser P, Mason G, Smith CR, von Konsky BR. 2005. Optimisation of vessel resistance using genetic algorithms and artificial neural networks. Hamburg: COMPIT.
- Matulja D, Dejhalla R, Bukovac O. 2010. Application of an artificial neural network to the selection of a maximum efficiency ship screw propeller. J Ship Prod Des. 26:199–205.
- Møller MF. 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6:525–533.
- More A, Deo M. 2003. Forecasting wind with neural networks. Mar Struct. 16:35–49.
- Papanikolaou A. 2014. Ship design: methodologies of preliminary design. London: Springer.
- Shaheed MH. 2004. Performance analysis of 4 types of conjugate gradient algorithms in the nonlinear dynamic modelling of a TRMS using feedforward neural networks. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics; October 10–13. The Hague, Netherlands: IEEE. p. 5985–5990.
- Singh V, Gupta I, Gupta H. 2007. ANN-based estimator for distillation using Levenberg–Marquardt approach. Eng Appl Artif Intell. 20:249–259.
- Zounemat-Kermani M. 2012. Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorol Atmos Phys. 117:181–192.
- Zurada JM. 1992. Introduction to artificial neural systems. St. Paul (MN): West Publishing Company.