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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-01-31
摘要
Reconstructing state-space dynamics from scalar data using time-delay embedding requires choosing values for the delay r and the dimension m. Both parameters are critical to the success of the procedure and neither is easy to formally validate. While embedding theorems do offer formal guidance for these choices, in practice one has to resort to heuristics, such as the average mutual 資訊 (AMI) method of Fraser & Swinney for r or the false near neighbor (FNN) method of Kennel et al. for m. Best practice suggests an iterative approach: one of these heuristics is used to make a good first guess for the corresponding free parameter and then an "asymptotic invariant"approach is then used to firm up its value by, e.g., computing the correlation dimension or 李雅普諾夫 exponent for a range of values and
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