Self-organizing data-driven prediction model of ship maneuvering fast-dynamics

Abstract

In this paper, to facilitate ship maneuvering fast-dynamics prediction which is critically required within motion planning and control, a self-organizing data-driven network with hierarchical pruning (SDN-HP) is innovated by virtue of fuzzy neural architecture. To be specific, the SDN-HP model is incrementally trained from an empty-hidden-node fuzzy neural network with recurrent velocities as inputs and outputs, and the rudder angle as exogenous command. With the aid of fuzzy neuron generation criteria, the SDN grows with increasing fuzzy rules such that fast-dynamics of ship velocities can be in-time fitted. To balance training accuracy and prediction generalization, hierarchical pruning mechanism is created by defining a new concept of fuzzy rule intensity, in order that hidden nodes with insignificant contributions can be dynamically removed, thereby enabling dynamics abstraction with parsimonious structure. With the aid of reference model of Esso Osaka tanker and KVLCC2 database, the SDN-HP model is sufficiently trained and applied to free-running maneuvering prediction in on-line manners. Experimental results show that the proposed SDN-HP framework achieves remarkable fast-dynamics prediction ability and outperforms state-of-the-art deep learning paradigm, e.g., LSTM and GRU.