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Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics.

Alberto Racca1, Luca Magri2

  • 1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|May 25, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances Echo State Networks (ESNs) for predicting chaotic systems by improving hyperparameter selection. New validation strategies based on chaos theory offer more robust and accurate forecasting of unpredictable dynamics.

Keywords:
Chaotic dynamical systemsReservoir ComputingRobustness

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Area of Science:

  • * Computational neuroscience and physics.
  • * Machine learning for time series analysis.

Background:

  • * Echo State Networks (ESNs), a type of Reservoir Computing, excel at predicting chaotic dynamics beyond their inherent predictability.
  • * Hyperparameter sensitivity in ESNs can significantly impact performance, necessitating robust selection methods.

Purpose of the Study:

  • * To improve the robustness of hyperparameter selection for ESNs in time-accurate chaotic system prediction.
  • * To develop and evaluate novel validation strategies tailored for chaotic time series forecasting.
  • * To compare Bayesian optimization with grid search for hyperparameter tuning.

Main Methods:

  • * Investigation of standard validation strategies for ESNs.
  • * Proposal of Recycle Validation and chaotic variants of existing strategies.
  • * Application of numerical tests on chaotic systems (Lorenz, Lorenz-96, Kuznetsov oscillator) using model-free and model-informed ESNs.

Main Results:

  • * Proposed validation strategies, grounded in chaos theory (e.g., Lyapunov time), outperform current methods.
  • * Demonstrated fundamental challenges in learning chaotic versus quasiperiodic solutions.
  • * Bayesian optimization showed promise in hyperparameter selection.

Conclusions:

  • * New validation strategies enhance the reliability of ESNs for chaotic system prediction.
  • * Principled, chaos-theory-based methods are adaptable to other Recurrent Neural Networks.
  • * Advances enable more robust design and application of ESNs and RNNs for chaotic dynamics.