Adaptive heave compensation via dynamic neural networks

Autor:	Dinesh Menon, D.G. Lainiotis, C.J. Charalampous, Konstantinos N. Plataniotis
Rok vydání:	2002
Předmět:	Adaptive control Recurrent neural network Artificial neural network Computer science Control theory Time delay neural network Motion estimation Control engineering Kalman filter Types of artificial neural networks Compensation (engineering)
Zdroj:	Proceedings of OCEANS '93.
DOI:	10.1109/oceans.1993.326005
Popis:	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. >
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5e66cc85c123a1c6c6df1cc0de5796e3 https://doi.org/10.1109/oceans.1993.326005 Zobrazit plný text záznamu