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...

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Detalles Bibliográficos
Autores principales: Meng, Xiangyi, Yang, Tong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626053/
https://www.ncbi.nlm.nih.gov/pubmed/34828189
http://dx.doi.org/10.3390/e23111491

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