opt = predictOptions creates
the default option set for predict.
Use dot notation to modify this option set. Any options that you do
not modify retain their default values.

Specify the output offsets for a two-output model as 2 and 5, respectively.

opt.OutputOffset = [2;5];

The software subtracts the offset value OutputOffset(i) from the i th output signal before using the output to predict the model response. The software then adds back these offsets to the predicted response to give the final response.

Specify Zero Initial Conditions for Model Prediction

Identify a fifth-order state-space model using the data.

sys = n4sid(z7,5);

Split the dataset into two parts.

zA = z7(1:15);
zB = z7(16:end);

Suppose that you want to compute the 10-step-ahead prediction of the response of the identified system for data zB. For initial conditions, use the signal values in zA as the historical record. That is, the input and output values for the time immediately preceding data in zB.

Generate the 10-step-ahead prediction for data zB using the specified initial conditions.

[yp,x0,Predictor] = predict(sys,zB,10,opt);

yp is the predicted model response, x0 are the initial states corresponding to the predictor model Predictor. You can simulate Predictor using x0 as initial conditions to reproduce yp.OutputData.

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: predictOptions('InitialCondition','z') specifies
zero initial conditions for the measured input-output data.

Handling of initial conditions, specified as the comma-separated
pair consisting of 'InitialCondition' and one of
the following values:

'z' — Zero initial conditions.

'e' — Estimate initial conditions
such that the prediction error for observed output is minimized.

For nonlinear grey-box models, only those initial states i that
are designated as free in the model (sys.InitialStates(i).Fixed
= false) are estimated. To estimate all the states of the
model, first specify all the Nx states of the idnlgrey model sys as
free.

for i = 1:Nx
sys.InitialStates(i).Fixed = false;
end

Similarly, to fix all the initial states to values specified
in sys.InitialStates, first specify all the states
as fixed in the sys.InitialStates property of the
nonlinear grey-box model.

'd' — Similar to 'e',
but absorbs nonzero delays into the model coefficients. The delays
are first converted to explicit model states, and the initial values
of those states are also estimated and returned.

Use this option for linear models only.

Vector or Matrix — Initial guess for state
values, specified as a numerical column vector of length equal to
the number of states. For multi-experiment data, specify a matrix
with Ne columns, where Ne is
the number of experiments. Otherwise, use a column vector to specify
the same initial conditions for all experiments. Use this option for
state-space (idss and idgrey) and nonlinear models (idnlarx, idnlhw,
and idnlgrey) 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 prediction:

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, configure the initial conditions
separately for each experiment by specifying InitialCondition as
a structure array with Ne elements. To specify
the same initial conditions for all experiments, use a single structure.

The software uses data2state to
map the historical data to states. If your model is not idss, idgrey, idnlgrey,
or idnlarx, 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.

x0obj — Specification object
created using idpar. Use this
object for discrete-time state-space (idss and idgrey)
and nonlinear grey-box (idnlgrey) models only.
Use x0obj to impose constraints on the initial
states by fixing their value or specifying minimum or maximum bounds.

Input signal offset for time-domain data, specified as the comma-separated
pair consisting of 'InputOffset' and one of the
following values:

[] — No input offsets.

A column vector of length Nu, where Nu is
the number of inputs. The software subtracts the offset value InputOffset(i) from
the ith input signal before using the input to
predict the model response.

Nu-by-Ne matrix
— For multi-experiment data, specify InputOffset as
an Nu-by-Ne matrix, where Ne is
the number of experiments. The software subtracts the offset value InputOffset(i,j) from
the ith input signal of the jth
experiment before prediction.

If you specify a column vector of length Nu,
then the offset value InputOffset(i) is subtracted
from the ith input signal of all the experiments.

Output signal offset for time-domain data, specified as the
comma-separated pair consisting of 'OutputOffset' and
one of the following values:

[] — No output offsets.

A column vector of length Ny, where Ny is
the number of outputs. The software subtracts the offset value OutputOffset(i) from
the ith output signal before using the output to
predict the model response. After prediction, the software adds the
offsets to the predicted response to give the final predicted response.

Ny-by-Ne matrix
— For multi-experiment data, specify OutputOffset as
an Ny-by-Ne matrix, where Ne is
the number of experiments. The software subtracts the offset value OutputOffset(i,j) from
the ith output signal of the jth
experiment before prediction.

If you specify a column vector of length Ny,
then the offset value OutputOffset(i) is subtracted
from the ith output signal of all the experiments.

After prediction, the software adds the removed offsets to the
predicted response to give the final predicted response.

'OutputWeight' — Weight of output for initial condition estimation [] (default) | 'noise' | matrix

Weight of output for initial condition estimation, specified
as the comma-separated pair consisting of 'OutputWeight' and
one of the following values:

[] — No weighting is used
by the software for initial condition estimation. This option is the
same as using eye(Ny) for the output weight, where Ny is
the number of outputs.

'noise' — The software uses
the inverse of the NoiseVariance property of the
model as the weight.

A positive, semidefinite matrix of dimension Ny-by-Ny,
where Ny is the number of outputs.

OutputWeight is relevant only for multi-output
models.

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