User`s guide

4 Nonlinear Black-Box Model Identification
Using Custom Regressors
In general, custom regressors are nonlinear functions of d elay ed input and
output data sample s. You can specify custom regre ssors, such as tan(u(t-1)),
u(t-1)
2
,oru(t-1)*y(t-3).
In the System Identification Tool GUI. You can create custom regressors
in the Model Regresso rs dialog box. For more information, see “How to
Estimate Nonlinear ARX Models in the GUI” on page 4-10.
At the comm and line. Use the
customreg or polyreg command to construct
custom regressors in terms of input-output variables. For m ore information,
see the corresponding reference page.
The linear b lock includes all standa rd a nd custo m reg ressors. H owever, you
can include specific standard and custom regressors in your nonlinear block to
fine-tune the model structure.
To get a linear-in-the-parameters ARX model structure, you can exclude
the nonlinear block from the model structure completely. When using
only a linear block with custom regressors, you can create the simplest
types of nonlinear models. In this case, the custom regressors capture
the nonlinearities and the estimation routine computes the weights of the
standard and custom regressors in the linear block to predict the output.
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