Phase autoencoder for limit-cycle oscillators
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914710763339776 |
|---|---|
| author | Yawata, Koichiro Fukami, Kai Taira, Kunihiko Nakao, Hiroya |
| author_facet | Yawata, Koichiro Fukami, Kai Taira, Kunihiko Nakao, Hiroya |
| contents | We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent the asymptotic phase of the oscillator. The trained autoencoder can perform two functions without relying on the mathematical model of the oscillator: first, it can evaluate the asymptotic phase and phase sensitivity function of the oscillator; second, it can reconstruct the oscillator state on the limit cycle in the original space from the phase value as an input. Using several examples of limit-cycle oscillators, we demonstrate that the asymptotic phase and phase sensitivity function can be estimated only from time-series data by the trained autoencoder. We also present a simple method for globally synchronizing two oscillators as an application of the trained autoencoder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06992 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Phase autoencoder for limit-cycle oscillators Yawata, Koichiro Fukami, Kai Taira, Kunihiko Nakao, Hiroya Adaptation and Self-Organizing Systems Machine Learning Chaotic Dynamics We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent the asymptotic phase of the oscillator. The trained autoencoder can perform two functions without relying on the mathematical model of the oscillator: first, it can evaluate the asymptotic phase and phase sensitivity function of the oscillator; second, it can reconstruct the oscillator state on the limit cycle in the original space from the phase value as an input. Using several examples of limit-cycle oscillators, we demonstrate that the asymptotic phase and phase sensitivity function can be estimated only from time-series data by the trained autoencoder. We also present a simple method for globally synchronizing two oscillators as an application of the trained autoencoder. |
| title | Phase autoencoder for limit-cycle oscillators |
| topic | Adaptation and Self-Organizing Systems Machine Learning Chaotic Dynamics |
| url | https://arxiv.org/abs/2403.06992 |