Using evolutionary neural networks to predict spatial orientation of a ship

The ability to precisely predict behavior of a ship can be useful for different ship systems, for example, dynamic positioning, video or artillery systems. To automatically maintain a ship position and heading, the dynamic positioning system has to know future behavior of the ship as exactly as possible. The same applies to the ship video and artillery systems which to continuously track a target have to predict both the location of the target and orientation of the ship deck in relation to the target in successive points in time.In this paper, evolutionary neural networks are proposed as ship behavior predictors. To perform the task, they are supplied with the information about ship spatial orientation (Euler angles) acquired from inertial navigational systems. In experiments reported in the paper, both monolithic and modular recurrent neural networks were tested. To build them, a neuro-evolutionary method called Assembler Encoding with Evolvable Operations was applied. As the point of reference for the networks two other prediction methods were used: the first is Linear Regression with Correction, i.e. the most effective method in preliminary experiments, whereas the second is Autoregressive Integrated Moving Average, which seems to be currently the most general tool for forecasting a time series.