Neural network application to ship position estimation

Author

Lainiotis, D.G. ; Plataniotis, K.N. ; Penon, D. ; Charalampous, C.J.

Author_Institution

Florida Inst. of Technol., Melbourne, FL, USA

fYear

1993

fDate

18-21 Oct 1993

Abstract

The real time estimation of ship motion is considered. The problem is viewed as an estimation/prediction problem for partially unknown systems. A neural estimator based on a dynamic recurrent neural network is considered. The model that describes the ship motion dynamics is presented, and the neural algorithm is tested and evaluated via extensive simulations. The results show that the new algorithm has excellent performance, and a significant saving in computational time is achieved

Keywords

Kalman filters; attitude control; geophysical techniques; geophysics computing; marine systems; motion estimation; neural nets; oceanographic techniques; data analysis; dynamic recurrent neural network; dynamics; estimation prediction problem; geophysical method; geophysics computing; measurement technique; motion estimation; neural algorithm; neural estimator; neural net; neural network; ocean; real time; ship; ship motion; ship position estimation; Computational modeling; Differential equations; Frequency; Marine vehicles; Motion control; Motion estimation; Neural networks; Recurrent neural networks; Target tracking; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

OCEANS '93. Engineering in Harmony with Ocean. Proceedings

Conference_Location

Victoria, BC

Print_ISBN

0-7803-1385-2

Type

conf

DOI

10.1109/OCEANS.1993.325979

Filename

325979