Before you can perform this task, you must have:
Performed any required data preprocessing operations. To improve the accuracy of your model, you should detrend your data. See Ways to Prepare Data for System Identification.
To estimate in the System Identification app using time-domain correlation analysis:
In the System Identification app, select Estimate > Correlation models to open the Correlation Model dialog box.
In the Time span (s) field, specify a scalar value as the time interval over which the impulse or step response is calculated. For a scalar time span T, the resulting response is plotted from -T/4 to T.
You can also enter a 2-D vector in the format
In the Order of whitening filter field, specify the filter order.
The prewhitening filter is determined by modeling the input as an autoregressive process of order N. The algorithm applies a filter of the form A(q)u(t)=u_F(t). That is, the input u(t) is subjected to an FIR filter A to produce the filtered signal u_F(t). Prewhitening the input by applying a whitening filter before estimation might improve the quality of the estimated impulse response g.
The order of the prewhitening filter, N,
is the order of the A filter. N equals
the number of lags. The default value of N is
which you can also specify as
In the Model Name field, enter the name of the correlation analysis model. The name of the model should be unique in the Model Board.
Click Estimate to add this model to the Model Board in the System Identification app.
In the Correlation Model dialog box, click Close.
Export the model to the MATLAB® workspace for further analysis by dragging it to the To Workspace rectangle in the System Identification app.
View the transient response plot by selecting the Transient resp check box in the System Identification app. For more information about working with this plot and selecting to view impulse- versus step-response, see Impulse and Step Response Plots.