Heave compensation prediction based on echo state network with correntropy induced loss function

Xiaogang Huang, Dongge Lei, Lulu Cai, Tianhao Tang and Zhibin Wang

PLOS ONE, 2019, vol. 14, issue 6, 1-10

Abstract: In this paper, a new prediction approach is proposed for ocean vessel heave compensation based on echo state network (ESN). To improve the prediction accuracy and enhance the robustness against noise and outliers, a generalized similarity measure called correntropy is introduced into ESN training, which is referred as corr-ESN. An iterative method based on half-quadratic minimization is derived to train corr-ESN. The proposed corr-ESN is used for the heave motion prediction. The experimental results verify its effectiveness.

Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217361 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 17361&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0217361

DOI: 10.1371/journal.pone.0217361

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().