Title :
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
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;
Conference_Titel :
OCEANS '93. Engineering in Harmony with Ocean. Proceedings
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-1385-2
DOI :
10.1109/OCEANS.1993.325979
