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Simulate linear models with uncertainty using Monte Carlo method

`simsd`

simulates linear models using
the Monte Carlo method. The command performs multiple simulations
using different values of the uncertain parameters of the model, and
different realizations of additive noise and simulation initial conditions. `simsd`

uses
Monte Carlo techniques to generate response uncertainty, whereas `sim`

generates the uncertainty using the
Gauss Approximation Formula.

`simsd(sys,udata)`

`simsd(sys,udata,N)`

`simsd(sys,udata,N,opt)`

`y = simsd(___)`

```
[y,y_sd]
= simsd(___)
```

`simsd(`

simulates
and plots the response of 10 perturbed realizations of the identified
model `sys`

,`udata`

)`sys`

. Simulation input data `udata`

is
used to compute the simulated response.

The parameters of the perturbed realizations of `sys`

are
consistent with the parameter covariance of the original model, `sys`

.
If `sys`

does not contain parameter covariance
information, the 10 simulated responses are identical. For information
about how the parameter covariance information is used to generate
the perturbed models, see Generating Perturbations of Identified Model.

`simsd(`

simulates
the system response using the simulation behavior specified in the
option set `sys`

,`udata`

,`N`

,`opt`

)`opt`

. Use `opt`

to
specify uncertainties in the initial conditions and include the effect
of additive disturbances.

The simulated responses are all identical if `sys`

does
not contain parameter covariance information, and you do not specify
additive noise or covariance values for initial states. You specify
these values in the `AddNoise`

and `X0Covariance`

options
of `opt`

.

`getcov`

| `rsample`

| `showConfidence`

| `sim`

| `simsdOptions`