DocumentCode
2116954
Title
Adaptive heave compensation via dynamic neural networks
Author
Lainiotis, D.G. ; Plataniotis, K.N. ; Menon, Dinesh ; Charalampous, C.J.
Author_Institution
Florida Inst. of Technol., Melbourne, FL, USA
fYear
1993
fDate
18-21 Oct 1993
Abstract
This paper discusses the problem of adaptive heave compensation. A new estimator based on dynamic recurrent neural networks is applied to this problem. It is shown that the new algorithm is well suited for online implementation and has excellent performance. Computational results via extensive simulations are provided to illustrate the effectiveness of the algorithm. A comparative evaluation with conventional methods is also provided
Keywords
Kalman filters; adaptive control; dynamics; motion estimation; recurrent neural nets; state estimation; Kalman filters; adaptive heave compensation; dynamic recurrent neural networks; motion estimation; neural estimator; online implementation; state estimation; Computational modeling; Frequency estimation; Neural networks; Petroleum; Recurrent neural networks; State estimation; State-space methods; Statistics; Underwater vehicles; Vehicle dynamics;
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.1993.326005
Filename
326005
Link To Document