Neural Networks Based Prediction Model for Vessel Track Control

Abstract

The problem of neural networks implementation for the construction of a predictive model for vessel track control was studied. It has been shown that the vessel track control problem may be considered as an approximation task, and neural networks may be implemented as universal approximating tools. The general structure of the prediction model, based on neural networks, has been developed. The model consists of several two-layered feedforward neural networks, which architectures satisfy the conditions of universal approximation properties. The analysis of the functions of the different neural networks in the prediction model has been performed. The network predicting WGS-84 geodetic latitude as a part of the predictive model has been constructed, trained and validated by using MATLAB software. The validation results show the good prediction precision of the net.