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Xiaogang Huang1,2, Dongge Lei2, Lulu Cai2

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A novel prediction method using correntropy-enhanced echo state networks (corr-ESN) improves ocean vessel heave compensation. This approach enhances accuracy and robustness against noisy data for reliable motion prediction.

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Area of Science:

  • Marine Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Ocean vessel heave motion significantly impacts operations and safety.
  • Accurate prediction of heave is crucial for effective compensation systems.
  • Existing methods may struggle with noise and outliers in motion data.

Purpose of the Study:

  • To propose a new prediction approach for ocean vessel heave compensation.
  • To enhance prediction accuracy and robustness using correntropy.
  • To introduce and validate the correntropy-enhanced echo state network (corr-ESN).

Main Methods:

  • Utilizing an echo state network (ESN) for time-series prediction.
  • Incorporating correntropy, a generalized similarity measure, into ESN training.
  • Developing an iterative training method based on half-quadratic minimization for corr-ESN.

Main Results:

  • The proposed corr-ESN model demonstrated effectiveness in heave motion prediction.
  • The inclusion of correntropy improved prediction accuracy.
  • The method showed enhanced robustness against noise and outliers.

Conclusions:

  • The corr-ESN approach offers a promising solution for ocean vessel heave compensation.
  • Correntropy integration enhances the performance of ESNs in complex marine environments.
  • The validated method provides a more reliable tool for predicting vessel heave motion.