Explicit construction of recurrent neural networks effectively approximating discrete dynamical systems
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916413991550976 |
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| author | Nakayama, Chikara Yoneda, Tsuyoshi |
| author_facet | Nakayama, Chikara Yoneda, Tsuyoshi |
| contents | We consider arbitrary bounded discrete time series originating from dynamical system with recursivity. More precisely, we provide an explicit construction of recurrent neural networks which effectively approximate the corresponding discrete dynamical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19278 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explicit construction of recurrent neural networks effectively approximating discrete dynamical systems Nakayama, Chikara Yoneda, Tsuyoshi Machine Learning Dynamical Systems We consider arbitrary bounded discrete time series originating from dynamical system with recursivity. More precisely, we provide an explicit construction of recurrent neural networks which effectively approximate the corresponding discrete dynamical systems. |
| title | Explicit construction of recurrent neural networks effectively approximating discrete dynamical systems |
| topic | Machine Learning Dynamical Systems |
| url | https://arxiv.org/abs/2409.19278 |