When you use this option set and simsd command to simulate the response of a model sys. The command returns the perturbed realizations of sys with additive disturbances added to each response.

Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.

Example: opt = simsdOptions('AddNoise',true','InputOffset',[5;0]) adds
default Gaussian white noise to the response model, and specifies
an input offset of 5 for the first of two model
inputs.

Simulation initial conditions, specified as one of the following:

'z' — Zero initial conditions.

Numerical column vector X0 of initial
states with length equal to the model order.

For multi-experiment data, specify a matrix with Ne columns,
where Ne is the number of experiments, to configure
the initial conditions separately for each experiment. Otherwise,
use a column vector to specify the same initial conditions for all
experiments.

Use this option for state-space models (idss and idgrey)
only. You can also specify the covariance of the initial state vector
in X0Covariance.

Structure with the following fields, which contain
the historical input and output values for a time interval immediately
before the start time of the data used in the simulation:

Field

Description

Input

Input history, specified as a matrix with Nu columns,
where Nu is the number of input channels. For time-series
models, use []. The number of rows must be greater
than or equal to the model order.

Output

Output history, specified as a matrix with Ny columns,
where Ny is the number of output channels. The
number of rows must be greater than or equal to the model order.

For multi-experiment data, you can configure the initial conditions
separately for each experiment by specifying InitialCondition as
a structure array with Ne elements. Otherwise,
use a single structure to specify the same initial conditions for
all experiments.

The software uses data2state to
map the historical data to states. If your model is not idss or idgrey,
the software first converts the model to its state-space representation
and then maps the data to states. If conversion of your model to idss is
not possible, the estimated states are returned empty.

X0Covariance — Covariance of initial states vector [] (default) | matrix

Covariance of initial states vector, specified as one of the
following:

Positive definite matrix of size Nx-by-Nx,
where Nx is the model order.

For multi-experiment data, specify as an Nx-by-Nx-by-Ne matrix,
where Ne is the number of experiments. For the k^{th} experiment, X0Covariance(:,:,k) specifies
the covariance of initial states X0(:,k).

[] — No uncertainty in the
initial states.

Use this option for state-space models (idss and idgrey)
when 'InitialCondition' is specified as a numerical
column vector X0. When you specify this option,
the software uses a different realization of the initial states to
simulate each perturbed model. Initial states are drawn from a Gaussian
distribution with mean InitialCondition and covariance X0Covariance.

Input signal offset, specified as a column vector of length Nu.
Use [] if there are no input offsets. Each element
of InputOffset is subtracted from the corresponding
input data before the input is used to simulate the model.

For multiexperiment data, specify InputOffset as:

An Nu-by-Ne matrix
to set offsets separately for each experiment.

A column vector of length Nu to
apply the same offset for all experiments.

Output signal offset, specified as a column vector of length Ny.
Use [] if there are no output offsets. Each element
of OutputOffset is added to the corresponding
simulated output response of the model.

For multiexperiment data, specify OutputOffset as:

An Ny-by-Ne matrix
to set offsets separately for each experiment.

A column vector of length Ny to
apply the same offset for all experiments.

Noise addition toggle, specified as a logical value indicating
whether to add noise to the response model. Set NoiseModel to true to
study the effect of additive disturbances on the response. A different
realization of the noise sequence, consistent with the noise component
of the perturbed system, is added to the noise-free response of that
system.

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