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950 bytes added ,  18:00, 1 June 2015
→‎Tutorials: Added False Nearest Neighbors
Please download it as the tutorial will reference it.
=== False Nearest Neighbors ===
This function uses a method to determine the minimum sufficient embedding dimension. We As a demonstration we will create a plot severalthat contains an Ikeda Map, a Henon Map and a Henon Map corrupted by 10% of Gaussian noise.{{Code|Analyzing false nearest neighbors|<syntaxhighlight lang="octave" style="font-size:13px"># Create mapsikd = ikeda (10000);hen = henon (10000);hen_noisy = hen + std (hen) * 0.02 .* (-6 + sum (rand ([size(hen), 12]), 3));# Create and plot false nearest neighbors[dim_ikd, frac_ikd] = false_nearest (ikd(:,1));[dim_hen, frac_hen] = false_nearest (hen(:,1));[dim_hen_noisy, frac_hen_noisy] = false_nearest (hen_noisy(:,1));plot (dim_ikd, frac_ikd, '-b*', 'markersize', 15,... dim_hen, frac_hen, '-r+', 'markersize', 15,... dim_hen_noisy, frac_hen_noisy, '-gx', 'markersize', 15);</syntaxhighlight>}}From this chart we can conclude the sufficient embedding dimension for each system. For a Henon Map {{Codeline|m &#61; 2}} is sufficient, but for an Ikeda map it is better to use {{Codeline|m &#61; 3}}.
=== Nonlinear Prediction ===
In this section we will demonstrate some functions from the 'Nonlinear Prediction' chapter of the TISEAN documentation (located [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node16.html#SECTION00050000000000000000 here]). For now this section will only demonstrate functions that are connected to the [http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/docs/chaospaper/node18.html#SECTION00052000000000000000 Simple Nonlinear Prediction] section. <br/>
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