User`s guide

3 Linear Model Identification
• None — The algorithm does not e stimate a noise model (C=D=1). This
option also sets Focus to
Simulation.
•
Order 1 — E stimates a noise model as a continuo us-time, first-order
ARMA model.
•
Order 2 — Estimates a noise model as a continuo u s-tim e, second-ord er
ARMA model.
At the com mand line. Specify the disturbance model as an argument in
the estimation command
pem. For example, us e this command to es timate a
first-order transfer function and a first-order noise model:
pem(data,'P1D','DisturbanceModel','AR MA1')
Tip You can type 'dis' instead of 'Disturban ceMo del'.
For a complete list of values for the DisturbanceModel model property, see
the
idproc reference page.
Options for Frequency-Weighing Focus
You can specify how the estimation algorithm weighs the fitatvarious
frequencies. This information supports the estimation procedures “How to
Estimate Process Models Using the GUI” on page 3-23 and “Estimating
Process Models at the Command Line” on page 3-29.
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.
3-38