Neural Networks Based Prediction Model for Vessel Track Control 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 twolayered 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 WGS84 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.

Research on Hovercraft Motion Prediction Based on Compound prediction Due to the strong coupling characteristic of the hovercraft during the movement and its own structural specificity, the hovercraft has poor maneuverability and is prone to danger during the navigation. In order to predict the attitude of the hovercraft and improve the hovercraft safety, this paper proposes a novel compound prediction model based on grey theory and improved Elman neural network. Grey prediction has the structural advantages of low number of original data and less effective information, but its processing ability to nonlinear systems is weak. Elman neural network has the function of selflearning and selforganizing to solve the nonlinear system. By combining the two models, a nonlinear system with less raw data can be processed. The model prediction results are determined by the individual prediction results and the weighted weight values of the model. Different weight coefficient ratios are assigned to different models to jointly obtain the results of the final compound prediction model. The simulation results show that this kind of compound prediction method has higher prediction accuracy with smaller predictive error in the hovercraft motion prediction application.