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Mendeley readers
Attention Score in Context
| Title |
Attractor reconstruction for non-linear systems: a methodological note
|
|---|---|
| Published in |
Mathematical Biosciences, May 2001
|
| DOI | 10.1016/s0025-5564(01)00053-0 |
| Pubmed ID | |
| Authors |
J.M. Nichols, J.D. Nichols |
| Abstract |
Attractor reconstruction is an important step in the process of making predictions for non-linear time-series and in the computation of certain invariant quantities used to characterize the dynamics of such series. The utility of computed predictions and invariant quantities is dependent on the accuracy of attractor reconstruction, which in turn is determined by the methods used in the reconstruction process. This paper suggests methods by which the delay and embedding dimension may be selected for a typical delay coordinate reconstruction. A comparison is drawn between the use of the autocorrelation function and mutual information in quantifying the delay. In addition, a false nearest neighbor (FNN) approach is used in minimizing the number of delay vectors needed. Results highlight the need for an accurate reconstruction in the computation of the Lyapunov spectrum and in prediction algorithms. |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 6% |
| United States | 1 | 3% |
| Unknown | 28 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 29% |
| Professor > Associate Professor | 5 | 16% |
| Student > Ph. D. Student | 4 | 13% |
| Professor | 2 | 6% |
| Student > Master | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 4 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 23% |
| Engineering | 5 | 16% |
| Medicine and Dentistry | 3 | 10% |
| Physics and Astronomy | 3 | 10% |
| Environmental Science | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 6 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 April 2018.
All research outputs
#5,452,627
of 25,394,764 outputs
Outputs from Mathematical Biosciences
#113
of 886 outputs
Outputs of similar age
#7,102
of 42,350 outputs
Outputs of similar age from Mathematical Biosciences
#1
of 5 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 886 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 42,350 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them