ANFIS Based Model for Ship Speed Prediction

Timely and precise speed prediction for large merchant ships is exceptionally important in almost all aspects of maritime transport. This paper explores the possibility of using the Adaptive Neuro-Fuzzy Inference System (ANFIS) for creating models for speed prediction of a bulk carrier depending on external hydrometeorological disturbances, namely wind speed, significant wave height and the speed of the sea current. Using a navigational simulator, an appropriate data base concerning the effect of the wind, waves and currents on the ship speed with regard to different directions of the disturbances was formed. This base was used to create data sets for training, testing and verifying the validity of the ANFIS model. An analysis of the effect of selecting the appropriate input-output membership functions while creating the ANFIS model was also performed in order to solve the above mentioned problem. The results gained by the created model are surely promising, which opens a perspective on the implementation of the created model in certain segments of maritime affairs.