Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 47-52 |
Number of pages | 6 |
Journal | Journal of Telecommunication, Electronic and Computer Engineering |
Volume | 9 |
Issue number | 1-3 |
Publication status | Published - 2017 |
Keywords
- Artificial neural network
- Direct inverse control
- Double-propeller
- USV