User`s guide

Troubleshooting Models
Troubleshooting Models
In this section...
“About Troubleshooting Models” on page 8-67
“Model Order Is Too High or Too Low” on page 8-67
“Nonlinearity Estimator Produces a Poor Fit” on page 8-68
“Substantial Noise in the System” on page 8-69
“Unstable Models” on page 8-69
“Missing Input Variables” on page 8-70
“Complicated Nonline arities” on page 8-71
About Troubleshooting Models
During validation, you might find that your model output fits the validation
data poorly. You might also find some unexpected or undesirable model
characteristics.
Ifthetipssuggestedinthesesections do not help improve your models, then
a good model might not be possible for this data. For example, your data
might have poor signal-to-noise ratio, large and nonstationary disturbances,
or varying sys tem properties.
Model Order Is To o High or Too Low
When the Model Output plot does not show a good fit, there is a good chance
that yo u need to try a different model order. System identification is largely
a trial-and-error process when selecting m odel structure and model order.
Ideally, you want the lowest-order m odel that adequately captures the system
dynamics.
You can estimate the model order a s described in “Preliminary Step –
Estimating Mo de l Orders and Input Delays” on page 3-49. Typically, you use
the suggeste d order as a starting point to estimate the lowest possible order
with different model structure s. After each estimation, you monitor the Model
Output and the Residual Analysis plots, and then adjust your settings for
the next estimation.
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