A novel vessel maneuvering model via GEBF based fuzzy eural networks

In this paper, a novel vessel maneuvering model is clearly identified by using the promising Generalized Ellipsoidal Function Based Fuzzy Neural Network (GEBF-FNN) method. Nonlinear differential equations of vessel maneuvering dynamics are used to establish the reference model implicating essential nonlinearities for GEBF-FNN based vessel maneuvering model (GEBF-FNN-VMM) identification. The GEBF-FNN-VMM starts with zero fuzzy rules and online recruits efficient fuzzy rules via rule node generation criteria and parameter estimation. The resultant GEBF-FNN-VMM reasonably captures essential maneuvering dynamics since the checking process validates the prediction performance with high accuracy. Finally, in order to demonstrate that the proposed scheme of system identification for vessel motion dynamics is effective, simulation studies are conducted on typical zig-zag maneuvers. Moreover, comprehensive comparisons are carefully presented. Simulation results indicate that the GEBF-FNN-VMM achieves promising performance in terms of approximation and prediction.