|System Identification Toolbox|
Simulate linear models.
m is an arbitrary
ue is an
iddata object, containing inputs only. The number of input channels in
ue must either be equal to the number of inputs of the model
m, or equal to the sum of the number of inputs and noise sources (= number of outputs). In the latter case the last inputs in
ue are regarded as noise sources and a noise-corrupted simulation is obtained. The noise is scaled according to the property
m, so in order to obtain the right noise level according to the model, the noise inputs should be white noise with zero mean and unit covariance matrix. If no noise sources are contained in
ue, a noise-free simulation is obtained.
y containing the simulated output, as an
init gives access to the initial states:
init = 'm' (default) uses the model
m's internally stored initial state.
init = 'z' uses zero initial state.
init = x0, where
x0is a column vector of appropriate length uses this value as the initial state.
The second output argument
ysd is the standard deviation of the simulated output.
m is a continuous-time model, it is first converted to discrete time with the sampling interval given by
ue taking into account the intersample behavior of the input (
ue.InterSample). See the sectionDiscrete and Continuous Time Models in the "Tutorial".
Simulate a given system
m0 (for example created by id
Validate a model by comparing a measured output
y with one simulated using an estimated model