## Estimate State-Space Models in System Identification App

Select

**Estimate**>**State Space Models**.The State Space Models dialog box opens.

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

**Help**.**Model name**displays the default model name. To change the name, enter a new 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.**Tip**When you do not know the model order, search for and select an order. For more information, see Estimate Model With Selected Order in the App.

Select the

**Continuous-time**or**Discrete-time**option to specify the type of model to estimate.You cannot estimate a discrete-time model if the working data is continuous-time frequency-domain data.

Specify the elements to include in the model structure, including feedthrough (

*D*matrix) and the disturbance component (*K*matrix.) Specify the model form, such as canonical form, by selecting from the options in**Form**. To specify delays, expand the**Delay**section.For more information about the type of state-space parameterization, see Supported State-Space Parameterizations.

Select the

**Estimation Options**tab to select the estimation method and configure the cost function.Select one of the methods in

**Estimation method**and configure the options. For more information about these methods, see State-Space Model Estimation Methods.Click

**Estimate**to estimate the model. A new model gets added to the System Identification app.

### Assigning Estimation Weightings

You can specify both how the estimation algorithm weights the fit at various frequencies
and what frequency range the app uses. In the app, set **Estimation Focus** to
one of the following options:

`Prediction`

— Uses the ratio of the input spectrum*U*to the inverse of the noise model*H*to weight the relative importance of data across the full frequency range. This weighting corresponds to minimizing one-step-ahead prediction, which typically favors the fit over a short time interval. Optimized for output prediction applications.`Simulation`

— Uses the input spectrum only, and not the noise model, for weighting. Optimized for output simulation applications.

You can apply a passband to limit the frequency range over which the estimation algorithm performs the fit.

For more information on estimation weighting, see the section Effects of
`Focus`

and `WeightingFilter`

Options in Loss Function and Model Quality Metrics.