User`s guide

Identifying Input-Output Polynomial Models
Option for Frequency-Weighing Focus
You can specify how the estimation algorithm weighs the fitatvarious
frequencies. This information supports the estimation procedures “How to
Estimate Polynomial Models in the GUI” on page 3-57 and “Using pem to
Estimate Polynomial Models” on page 3-61.
In the System Identifi cation Tool GUI. Set Focus to one of t he following
options:
•
Prediction — Uses the inverse of the noise model H to weigh the relative
importance of how closely to fit the data in various frequency ranges.
Corresponds to minimizing one-step-ahead prediction, which typically
favors the fit over a short time interval. O ptimized for output prediction
applications.
•
Simulation — Uses the input spectrum to weigh the relative importance of
the fitinaspecific frequency range. Does not use the noise model to w eigh
the relative importance of how closely to fit the data in various frequency
ranges. Optimized for output simulation applications.
•
Stability — Estimates the best stable model. For more information about
model stability, see “Unstable Models” on page 8-69.
•
Filter —Specifyacustomfilter to open the Estimation Focus dialog box,
where you can enter a filter, as described in “Simple Passband Filter” on
page 1-111 or “Defining a Custom Filter” on page 1-112. This prefiltering
applies only for estimating the d ynamics from input to output. The
disturbance model is determined from the unfiltered estimation data.
At the command line. Specifythefocusasanargumentinthe
model-estimation command using the same options as in the GUI. For
example, use this command to estimate an AR X model and emphasize the
frequency content related to the input spectrum only:
m=arx(data,[2 2 3],'Focus','Simul ation')
This Foc us setting might produce more accurate sim ulation results.
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