User`s guide

Identifying State-Space Models
validatin g your mod e l, see “Overvi ew of Model Valida t ion and Plots ” on
page 8-2.
Tip You can export the model to the MATLA B workspace for further analysis
by dragging it to the To Workspace rectangle in the System Identification
Tool GUI.
How to Estimate State-Space Models at the Command
Line
• “Supported State-Space Models” on page 3-87
• “Estim a t ing State-Space Models U s in g pe m and n4si d” on page 3-87
• “Common Properties to Specify Model Estimation” on page 3-88
• “ChoosingtoEstimateD,K,andX0Matrices”onpage3-89
Supported State-Space Models
You can only estimate discrete-time state-space models with free
parameterization. Continuous state-space models are available for canonical
and structured parameterizations.
Estimating State-Space Models Using pem and n4sid
You can estimate continuous-time and discrete- t ime polynomial mo de l using
the iterative estimation com m and
pem that minimizes the prediction errors
to obtain maxim u m-likelihood values. You can also u se the n on i te r ati ve
subspace command
n4sid.
You mus t have already estimated the model order, as d escribe d in
“Preliminary Step – Estimating State-Space Model Orders” on page 3-79. You
use this model order as input to the estimation functions.
Use the following general syntax to both configure and estimate state-space
models:
m = pem(data,n,
'nk',nk,
3-87