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Forecasting the Forced van der Pol Equation with Frequent Phase Shifts Using Reservoir Computing

arXiv 2404.14651 ↗First posted 23 Apr 2024nlin.AO cs.LG cs.NEcited by 0

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Abstract (arXiv)

We tested the performance of reservoir computing (RC) in predicting the dynamics of a certain non-autonomous dynamical system. Specifically, we considered a van del Pol oscillator subjected to periodic external force with frequent phase shifts. The reservoir computer, which was trained and optimized with simulation data generated for a particular phase shift, was designed to predict the oscillation dynamics under periodic external forces with different phase shifts. The results suggest that if the training data have some complexity, it is possible to quantitatively predict the oscillation dynamics exposed to different phase shifts. The setting of this study was motivated by the problem of predicting the state of the circadian rhythm of shift workers and designing a better shift work schedule for each individual. Our results suggest that RC could be exploited for such applications.

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17 of 69 references.

References · 17

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  3. 2021Next Generation Reservoir Computingcited by 160
  4. 2019Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamicscited by 134
  5. 2018Multi-Step Prediction of Dynamic Systems with Recurrent Neural Networkscited by 25
  6. 2021Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observationscited by 21
  7. 2021Learning strange attractors with reservoir systemscited by 17
  8. 2021Reservoir Computing as a Tool for Climate Predictability Studiescited by 13
  9. 2023Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delayscited by 10
  10. 2022Deep learning delay coordinate dynamics for chaotic attractors from partial observable datacited by 8
  11. 2023Reservoir Computing with Noisecited by 8
  12. 2019Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observationcited by 4
5 more
  1. 2023Inferring Attracting Basins of Power System with Machine Learningcited by 4
  2. 2021PyRCN: A Toolbox for Exploration and Application of Reservoir Computing Networkscited by 3
  3. 2022Delay Embedded Echo-State Network: A Predictor for Partially Observed Systemscited by 3
  4. 2022Digital twins of nonlinear dynamical systemscited by 2
  5. 2020LSTM-based Anomaly Detection for Non-linear Dynamical Systemcited by 1

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