Development of Self-Organizing Maps neural networks based control system for a boat model

This paper describes the development of a controller system for a developed double-propeller boat model using the unsupervised learning neural networks,namely the self-organizing maps (som). the performance characteristics of the proposed som-based controller are then compared with that of the well-known back-propagation neural networks (bpnn)-based controller through a direct inverse control scheme. experimental results showed that the som-based controller can produce a low error,even lower than that of the widely used bpnn-based controller. furthermore,the computational cost of the som-based controller is found to be more than 700 times faster than that of the bpnn-based controller. these findings suggest that the utilization of the proposed som-based controller for the control of a boat is highly effective.