NEURAL ESTIMATION OF THE WEIGHT OF METALLIC HULL FOR TRANSPORT SHIPS
YU Minghu, XU Changwen · Shipbuilding of China · 1997
This paper deals with the neural estimation of the weight of metallic hull for transport ships based on the multilayer feed forward neural network model trained by using the backpropagation learning algorithm. It is shown by the computational results for bulk carriers and tankers that massively parallel, interconnected networks of nonlinear analog neurons are most effective in the hull weight estimation.