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Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave

Lumeng Huang1, Xiaogang Deng2, YingChun Bo3

  • 1College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, China; National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao 266580, China.

ISA Transactions
|August 30, 2021
PubMed
Summary

A new delayed deep cycle reservoir with regular jumps (CRJ) model enhances time series prediction by increasing memory capacity. This advanced CRJ model, optimized with evolutionary algorithms, improves ship heave motion prediction accuracy.

Keywords:
Cycle reservoir with regular jumpsDeep learningDifferential evolutionTime series prediction

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Area of Science:

  • * Computational intelligence
  • * Time series analysis
  • * Deep learning

Background:

  • * Cycle reservoir with regular jumps (CRJ) is an emerging method for time series analysis, including ship heave motion prediction.
  • * The shallow structure of single CRJ models limits memory capacity, leading to suboptimal prediction performance.
  • * Enhanced dynamic characteristic description is needed for complex time series data.

Purpose of the Study:

  • * To introduce a delayed deep CRJ model integrating deep learning with delay links for improved time series analysis.
  • * To enhance the memory capacity of CRJ models for more accurate ship heave motion prediction.
  • * To develop an automated parameter optimization method for the delayed deep CRJ model.

Main Methods:

  • * Development of a delayed deep CRJ model with multiple serial reservoirs and delay links.
  • * Integration of a deep learning framework to enlarge model memory capacity.
  • * Application of a heuristic evolutionary optimization scheme based on stepwise differential evolution for automatic parameter determination.

Main Results:

  • * The delayed deep CRJ model demonstrated enlarged memory capacity compared to the basic CRJ model.
  • * The stepwise differential evolution algorithm effectively determined model parameters, avoiding manual setting issues.
  • * Numerical and experimental validation confirmed superior prediction performance of the delayed deep CRJ model.

Conclusions:

  • * The delayed deep CRJ model offers a significant improvement over the basic CRJ method for time series prediction tasks.
  • * Integrating deep learning and evolutionary optimization enhances the capability of reservoir computing models.
  • * The proposed method provides a robust and accurate approach for ship heave motion prediction.