Please use this identifier to cite or link to this item:
https://doi.org/10.1103/PhysRevE.72.027204
| DC Field | Value | |
|---|---|---|
| dc.title | Postprocessing methods for finding the embedding dimension of chaotic time series | |
| dc.contributor.author | Por, L.T. | |
| dc.contributor.author | Puthusserypady, S. | |
| dc.date.accessioned | 2014-06-17T03:02:12Z | |
| dc.date.available | 2014-06-17T03:02:12Z | |
| dc.date.issued | 2005-08 | |
| dc.identifier.citation | Por, L.T., Puthusserypady, S. (2005-08). Postprocessing methods for finding the embedding dimension of chaotic time series. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 72 (2) : -. ScholarBank@NUS Repository. https://doi.org/10.1103/PhysRevE.72.027204 | |
| dc.identifier.issn | 15393755 | |
| dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/57096 | |
| dc.description.abstract | One problem when using the global false nearest-neighbors (GFNN) method and Cao's method to estimate embedding dimension is that their effectiveness is affected by the ratio of signal power to noise power (SNR). Simple models are proposed to explain the curves commonly obtained when using the GFNN method and Cao's method. Methods are proposed for systematically estimating the embedding dimension. Prior information is incorporated to improve the estimates. © 2005 The American Physical Society. | |
| dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1103/PhysRevE.72.027204 | |
| dc.source | Scopus | |
| dc.type | Article | |
| dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
| dc.description.doi | 10.1103/PhysRevE.72.027204 | |
| dc.description.sourcetitle | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | |
| dc.description.volume | 72 | |
| dc.description.issue | 2 | |
| dc.description.page | - | |
| dc.description.coden | PLEEE | |
| dc.identifier.isiut | 000231564100135 | |
| Appears in Collections: | Staff Publications | |
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