Open AccessHeave compensation prediction based on echo state network with correntropy induced loss function
Author(s) -
Xiaogang Huang,
Dongge Lei,
Lulu Cai,
Tianhao Tang,
Zhibin Wang
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0217361
Subject(s) - echo state network , outlier , robustness (evolution) , computer science , echo (communications protocol) , noise (video) , minification , artificial intelligence , artificial neural network , recurrent neural network , computer network , biochemistry , chemistry , image (mathematics) , gene , programming language
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.