APPLICATION OF AN ARTIFICIAL NEURAL NETWORK AND MULTIPLE NONLINEAR REGRESSION TO ESTIMATE CONTAINER SHIP LENGTH BETWEEN PERPENDICULARS

Abstract

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.