Predictions of oil/chemical tanker main design parameters using computational intelligence techniques
Ship design and construction are complicated and expensive processes. In the pre-design stage, before the construction according to some special rules, determination of the main ship parameters is very important. In this study, instead of traditional methods, the oil/chemical tanker main design parameters are estimated by 18 computational intelligence methods. Therefore, all the data of 114 tankers in operation are used in the experiments in order to estimate a parameter from the remaining ones. Main result of this article is to show that, except for the speed parameter, the main parameters of tankers can be estimated sufficiently well for pre-design stage without having to apply conventional but arduous ship modeling experiments.
computational intelligence machine learning neural networks naval engineering ship design main parameters