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Randomly distributed embedding making short-term high-dimensional data predictable
Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achiev...
שמור ב:
| הוצא לאור ב: | Proc Natl Acad Sci U S A |
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| Main Authors: | , , , , |
| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
National Academy of Sciences
2018
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| נושאים: | |
| גישה מקוונת: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6205453/ https://ncbi.nlm.nih.gov/pubmed/30297422 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1802987115 |
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