Ship roll motion prediction based on ℓ1 regularized extreme learning machine

PLoS One. 2018 Oct 30;13(10):e0206476. doi: 10.1371/journal.pone.0206476. eCollection 2018.

Abstract

In this paper, a new method is proposed for prediction of ship roll motion based on extreme learning machine (ELM). To improve the prediction accuracy and avoid over or under fitting, two techniques are adopted to select the appropriate structure of ELM. First, the inputs of the ELM are selected from the roll motion time series using Lipschitz quotient method. Second, the number of hidden layer nodes is determined via ℓ1 regularized technique. Finally, the ℓ1 regularized ELM is solved by least angle regression (LAR) algorithm. The effectiveness of the proposed method is demonstrated by ship roll motion prediction experiments based on the real measured ship roll motion time series.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Machine Learning
  • Motion
  • Neural Networks, Computer
  • Ships / methods*

Grants and funding

This work is partially supported by the National Natural Science Foundation of China (Nos. 61403218, 61503336 to BLG). The commercial affiliation State GRID Quzhou Power Supply Company provided support in the form of salaries for author Wei Yang, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.