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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(`

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`