Skip to main content
padlock icon - secure page this page is secure

The Performance Comparison of Classical and Fast Backpropagation Neural Network Algorithms for Rudder Roll Stabilization of Fishing Vessels

Buy Article:

$40.00 + tax (Refund Policy)

This paper presents the performance comparative results of the classic backpropagation algorithms (CBAs) and fast backpropagation algorithms (FBAs) of neural networks applied for the rudder roll stabilizer (RRS) systems. The rudder is used to keep stability of the ship motions by minimizing errors. In this paper, neural networks are designed to reduce the errors considerably by using rudders for simultaneous course keeping and roll damping. Simulation results show the effectiveness of FBA approach for rudder roll stabilization of fishing vessels when compared with CBA results.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics


Document Type: Research Article

Publication date: July 1, 2008

More about this publication?
  • Marine Technology is dedicated to James Kennedy, 1867-1936, marine engineer, and longtime member of the Society, in recognition and appreciation of his sincere and generous interest in furthering the art of ship design, shipbuilding, ship operation, and related activities.

    The Technical papers in this quarterly flagship journal cover a broad spectrum of research on the latest technological breakthroughs, trends, concepts, and discoveries in the marine industry. SNAME News is packed with Society news and information on national, section, and local levels as well as updates on committee activities, meetings, seminars, professional conferences, and employment opportunities.

    For access to Volume 47 Issue 2 and later, please contact SNAME
  • Information for Authors
  • Membership Information
  • Volume 47 Issue 2 and later
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more