User`s guide

8 Model Analysis
Computing Model Uncertainty
In this section...
“Why An alyze Model Uncertainty?” on page 8-64
“What Is Model Covariance?” on page 8-64
“Viewing Model Uncertainty Information” on page 8-65
Why Analyze Model Uncer tainty?
In addition to estimating model parameters, the to olbox algorithms also
estimate variability of the model parameters that result from random
disturbances in the output.
Understanding model variability helps you to understand how different your
model parameters w ould be if you repeated the estimation using a different
data set (with the same input sequence as the original data set) and the same
model structure.
When validating yo ur pa ra m etric models, check the uncertainty values.
Large uncertaintie s in the parameters might be caused by high model orders ,
inadequate excitation, and poo r signal-to-noise ratio in the data.
Note You can get model uncertainty data forlinearparametricblack-box
models, and both linear and non linear grey-box models. S upported model
objects include
idproc, idpoly, idss, idarx , idgrey, idfrd,andidnlgrey.
What Is Model Covariance?
Uncertainty in the model is called model covariance.
If you estimate m odel uncertainty data, this inform ation is stored in the
Model.CovarianceMatrix model property. The covariance matrix is used to
compute all uncertainties in model output, Bode plots, residual plots, and
pole-zero plots.
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