Forecasting the Forced van der Pol Equation with Frequent Phase Shifts Using Reservoir Computing
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909233625169920 |
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| author | Kuno, Sho Kori, Hiroshi |
| author_facet | Kuno, Sho Kori, Hiroshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14651 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecasting the Forced van der Pol Equation with Frequent Phase Shifts Using Reservoir Computing Kuno, Sho Kori, Hiroshi Adaptation and Self-Organizing Systems Machine Learning Neural and Evolutionary Computing 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. |
| title | Forecasting the Forced van der Pol Equation with Frequent Phase Shifts Using Reservoir Computing |
| topic | Adaptation and Self-Organizing Systems Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2404.14651 |