An Artificial Neural Network Controller for Course Changing Manoeuvring
Yaseen Adnan Ahmed1, Iwan Zamil Mustaffa Kamal2, Mohammad Abdul Hannan3
1Yaseen Adnan Ahmed, Maritime Engineering Technology Department, Umiversity Kuala Lumpur, Malaysian Institute of Marine Engineering Technology, Lumut, Malaysia.
2Iwan Zamil Mustaffa Kamal, Maritime Engineering Technology Department, Umiversity Kuala Lumpur, Malaysian Institute of Marine Engineering Technology, Lumut, Malaysia.
3Mohammad Abdul Hannan, Marine Technology Department, Newcastle University in Singapore, Singapore.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5714-5719 | Volume-8 Issue-12, October 2019. | Retrieval Number: L40031081219/2019©BEIESP | DOI: 10.35940/ijitee.L4003.1081219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An Artificial Neural Network is a well-known AI technique for replicating human brain and offering suitable solution for any unpredictable complicated problem. Taking the advantage of it, this research will analyse the applicability of Neural Network Controller for ship manoeuvring, such as course changing. To train the controller, optimized teaching data are used to keep the consistency in the data as it could enhance the learning ability of the controller while training. A double layered feed-forward neural network and back propagation method are found suitable for this purpose. Later-on, simulations are done to justify the effectiveness of the trained controller for unknown situations.
Keywords: Index Terms: Artificial Neural Network, Intelligent Controller, Numerical Analysis, Optimisation, Ship Manoeuvring
Scope of the Article: Artificial Intelligent