Tip
For more information on the options in the dialog box, click Help.

Specify a model name by clicking adjacent to Model
name. The name of the model must be unique in the Model
Board.

Select the Specify value option
(if not already selected) and specify the model order in the edit
field. Model order refers to the number of states in the state-space
model.

Select the Continuous-time or Discrete-time option
to specify whether to estimate a continuous- or discrete-time model.

You cannot estimate a discrete-time model if the working data
is continuous-time frequency-domain data.

Expand the Model Structure Selection section
to configure the model structure, such as canonical form, whether
to estimate the disturbance component (K matrix),
and specification of feedthrough and input delays.

Expand the Estimation Options section
to select the estimation method, and configure the cost function.

Select one of the following Estimation Method from
the drop-down list and configure the options. For more information
about these methods, see State-Space Model Estimation Methods.

In the N4Weight drop-down list,
specify the weighting scheme used for singular-value decomposition
by the N4SID algorithm.

The N4SID algorithm is used both by the subspace and Prediction
Error Minimization (PEM) methods.

In the N4Horizon field, specify
the forward and backward prediction horizons used by the N4SID algorithm.

The N4SID algorithm is used both by the subspace and PEM methods.

In the Focus drop-down list,
select how to weigh the relative importance of the fit at different
frequencies. For more information about each option, see Assigning Estimation Weightings.

Select the Allow unstable models check
box to specify whether to allow the estimation process to use parameter
values that may lead to unstable models.

Setting this option is same as setting the estimation option Focus to 'prediction' at
the command line. An unstable model is delivered only if it produces
a better fit to the data than other stable models computed during
the estimation process.

Select the Estimate covariance check
box if you want the algorithm to compute parameter uncertainties.

Effects of such uncertainties are displayed on plots as model
confidence regions. Skipping uncertainty computation reduces computation
time for complex models and large data sets.

Select the Display progress check
box to open a progress viewer window during estimation.

In the N4Weight drop-down list,
specify the weighting scheme used for singular-value decomposition
by the N4SID algorithm.

The N4SID algorithm is used both by the subspace and Prediction
Error Minimization (PEM) methods.

In the N4Horizon field, specify
the forward and backward prediction horizons used by the N4SID algorithm.

The N4SID algorithm is used both by the subspace and PEM methods.

In the Focus drop-down list,
select how to weigh the relative importance of the fit at different
frequencies. For more information about each option, see Assigning Estimation Weightings.

Select the Allow unstable models check
box to specify whether to allow the estimation process to use parameter
values that may lead to unstable models.

Setting this option is same as setting the estimation option Focus to 'prediction' at
the command line. An unstable model is delivered only if it produces
a better fit to the data than other stable models computed during
the estimation process.

Select the Estimate covariance check
box if you want the algorithm to compute parameter uncertainties.

Effects of such uncertainties are displayed on plots as model
confidence regions. Skipping uncertainty computation reduces computation
time for complex models and large data sets.

Select the Display progress check
box to open a progress viewer window during estimation.

Tip
If you get an inaccurate fit, try setting a specific method
for handling initial states rather than choosing it automatically.

Click Regularization to obtain
regularized estimates of model parameters. Specify the regularization
constants in the Regularization Options dialog box. To learn more,
see Regularized Estimates of Model Parameters.

Specify Iteration Options to
specify options for controlling the iterations. The Options for Iterative
Minimization dialog box opens.

In the Options for Iterative Minimization dialog box, you can
specify the following iteration options:

Search Method — Method
used by the iterative search algorithm. Search method is auto by
default. The descent direction is calculated using gn (Gauss-Newton), gna (Adaptive
Gauss-Newton), lm (Levenberg-Marquardt), lsqnonlin (Trust-Region
Reflective Newton), and grad (Gradient
Search) successively at each iteration until a sufficient reduction
in error is achieved.

Output weighting — Weighting
applied to the loss function to be minimized. Use this option for
multi-output estimations only. Specify as 'noise' or
a positive semidefinite matrix of size equal the number of outputs.

Maximum number of iterations —
Maximum number of iterations to use during search.

Termination tolerance —
Tolerance value when the iterations should terminate.

Error threshold for outlier penalty —
Robustification of the quadratic criterion of fit.

In the Regularization Kernel drop-down
list, select the regularizing kernel to use for regularized estimation
of the underlying ARX model. To learn more, see Regularized Estimates of Model Parameters.

In the ARX Orders field, specify
the order of the underlying ARX model. By default, the orders are
automatically computed by the estimation algorithm. If you specify
a value, it is recommended that you use a large value for nb order.
To learn more about ARX orders, see arx.

In the Focus drop-down list,
select how to weigh the relative importance of the fit at different
frequencies. For more information about each option, see Assigning Estimation Weightings.

In the Reduction Method drop-down
list, specify the reduction method:

Truncate — Discards
the specified states without altering the remaining states. This method
tends to product a better approximation in the frequency domain, but
the DC gains are not guaranteed to match.

MatchDC — Discards
the specified states and alters the remaining states to preserve the
DC gain.

Select the Estimate covariance check
box if you want the algorithm to compute parameter uncertainties.

Effects of such uncertainties are displayed on plots as model
confidence regions. Skipping uncertainty computation reduces computation
time for complex models and large data sets.

Select the Display progress check
box to open a progress viewer window during estimation.

Validate the model by selecting the appropriate response
type in the Model Views area of the app. For
more information about validating models, see Validating Models After Estimation.

Export the model to the MATLAB^{®} workspace for
further analysis by dragging it to the To Workspace rectangle
in the app.