System Identification Toolbox    

Simulate linear models.



m is an arbitrary idmodel object.

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.NoiseVariance in 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.

sim returns y containing the simulated output, as an iddata object.

init gives access to the initial states:

The second output argument ysd is the standard deviation of the simulated output.

If 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 idpoly).

Validate a model by comparing a measured output y with one simulated using an estimated model m.

See Also

iddata, idpoly, idarx, idss, idgrey, simsd

  setpname simsd