Development of Self-Organizing Maps Neural Networks Based Control System for a Boat Model

Authors

  • Karlisa Priandana Computational Intelligence and Intelligent Systems Research Group, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru Universitas Indonesia, Depok, West Java, Indonesia. Department of Computer Science, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Kampus IPB Dramaga, Bogor, West Java, Indonesia.
  • Benyamin Kusumoputro Computational Intelligence and Intelligent Systems Research Group, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru Universitas Indonesia, Depok, West Java, Indonesia.

Keywords:

Artificial Neural Network, Direct Inverse Control, Double Propeller, USV,

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.

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Published

2017-03-15

How to Cite

Priandana, K., & Kusumoputro, B. (2017). Development of Self-Organizing Maps Neural Networks Based Control System for a Boat Model. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-3), 47–52. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1742