Estimate Correlation Dimension
Estimate the correlation dimension of a uniformly sampled signal in the Live Editor
Description
The Estimate Correlation Dimension task lets you interactively estimate the correlation dimension of a uniformly sampled signal. The task automatically generates MATLAB® code for your live script. For more information about Live Editor tasks generally, see Add Interactive Tasks to a Live Script.
Correlation dimension is the measure of dimensionality of the space occupied by a set of random points. Correlation dimension is estimated as the slope of the correlation integral versus the range of radius of similarity. Use correlation dimension as a characteristic measure to distinguish between deterministic chaos and random noise, to detect potential faults.
Open the Task
To add the Estimate Correlation Dimension task to a live script in the MATLAB Editor:
On the Live Editor tab, select Task > Estimate Correlation Dimension.
In a code block in your script, type a relevant keyword, such as
correlation dimension
orcorrelation dimension
. SelectEstimate Correlation Dimension
from the suggested command completions.
Examples
Related Examples
Parameters
Select SignalSignal
— Uniformly sampled time-domain signal
array | timetable
Select a uniformly sampled time-domain signal in array or timetable format from the MATLAB workspace. If the signal has multiple columns, the Estimate Correlation Dimension task computes the correlation dimension by treating it as a multivariate signal. If the signal is a row vector, then the Estimate Correlation Dimension task treats it as a univariate signal.
Signal Type
— Type of selected signal
'Time Domain
' | 'Phase space
'
Specify the type of the selected signal as either 'Time Domain
'
or 'Phase space
'. If you specify the signal type as:
'
Time Domain
', then also specify the embedding dimension and time lag for your signal.'
Phase space
', then the Estimate Correlation Dimension task automatically infers the embedding dimension and time lag using the phase space information.
Embedding Dimension
— Number of dimensions of phase space vectors
scalar | vector
Specify the number of dimensions of phase space vectors as a scalar or vector from the MATLAB workspace. When you specify the embedding dimension as a scalar, then the Estimate Correlation Dimension task uses the same embedding dimension value to estimate the value of correlation dimension for all the columns of the uniformly sampled signal.
The Embedding Dimension
drop down is active only when you
specify the signal type as 'Time Domain
'. For phase space signals,
the Estimate Correlation Dimension task automatically
computes the embedding dimension from the phase space data.
If you do not know the value of embedding dimension for your signal, then you can compute it using the Reconstruct Phase Space task.
Time Lag
— Time lag between successive phase vectors
scalar | vector
Specify time lag between successive phase vectors as a scalar or vector from the MATLAB workspace. When you specify the time lag as a scalar, then the Estimate Correlation Dimension task uses the same time delay value to estimate the value of correlation dimension for all the columns of the uniformly sampled signal. If you specify the embedding dimension as a vector, then specify the time lag also as a vector of the same length.
The Time Lag
drop down is active only when you specify
the signal type as 'Time Domain
'. For phase space signals, the
Estimate Correlation Dimension task automatically
computes the time lag from the phase space data.
If you do not know the value of time lag for your signal, then you can compute it using the Reconstruct Phase Space task.
Similarity Radius Min
— Minimum radius of similarity
max radius/1000
(default) | scalar
Specify the minimum radius of similarity to be used to compute the number of with-in range points for correlation dimension estimation. Try different values such that the linear fit line aligns with the original data line in the plot.
Similarity Radius Max
— Maximum radius of similarity
0.2*sqrt(trace(cov(signal)))
(default) | scalar
Specify the maximum radius of similarity to be used to compute the number of with-in range points for correlation dimension estimation. Try different values such that the linear fit line aligns with the original data line in the plot.
Number of Points
— Number of points between the minimum and maximum radius
10 (default) | positive scalar integer
Specify the number of points between the maximum and minimum radius of similarity. Choose an appropriate number of points based on the resolution required to compute the correlation dimension.
Output Display
— Toggle result display in the Live Editor output
on (default) | off
Toggle to display the value of correlation dimension in the Live Editor output.
Version History
Introduced in R2019b