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Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting
Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference [Formula: see text] between consecutive steps is so large that the extracted time series looks apparently random. Here, we introduce a new long-short-term-memor...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626053/ https://www.ncbi.nlm.nih.gov/pubmed/34828189 http://dx.doi.org/10.3390/e23111491 |