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
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;
Conference_Titel :
OCEANS '93. Engineering in Harmony with Ocean. Proceedings
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-1385-2
DOI :
10.1109/OCEANS.1993.326005
