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

Example: '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 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.

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 [].

Output

Output history, specified as a matrix with Ny columns,
where Ny is the number of output channels.

For each field, if the respective signals are constant before
the start time of the simulation, use a row vector to specify the
constant values.

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.

'model' — Initial conditions
as specified in the InitialStates property of the
model. Use this option for idnlgrey models only.
This option corresponds to the default initial conditions for idnlgrey models
when no simOptions option set is used.

'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.

[] — No uncertainty in the
initial states.

Use this option only for state-space models (idss and idgrey)
when 'InitialCondition' is specified as a column
vector. Use this option to account for initial condition uncertainty
when computing the standard deviation of the simulated response of
a model.

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.

'NoiseData' — Noise signal data [] (default) | matrix | cell array of matrices

Noise signal data specified as one of the following:

[] — Default Gaussian white
noise.

Matrix with N_{s} rows
and N_{y} columns, where N_{s} is
the number of input data samples, and N_{y} is
the number of outputs. Each matrix entry is scaled according to NoiseVariance property
of the simulated model and added to the corresponding output data
point. To set NoiseData at a level that is consistent
with the model, use white noise with zero mean and a unit covariance
matrix.

Cell array of N_{e} matrices,
where N_{e} is the number of
experiments for multiexperiment data. Use a cell array to set the NoiseData separately
for each experiment, otherwise set the same noise signal for all experiments
using a matrix.

NoiseData is the noise signal, e(t),
for the model

$$y(t)=Gu(t)+He(t).$$

Here,G is the transfer function from the
input, u(t), to the output, y(t),
and H is the noise transfer function.

NoiseData is used for simulation only when AddNoise is
true.