# predictOptions

Option set for `predict`

## Description

creates
an option set with options specified by one or more `opt`

= predictOptions(`Name,Value`

)`Name,Value`

pair
arguments.

## Examples

### Specify Output Offset for Predicting Model Response

Create a default option set for model prediction.

opt = predictOptions;

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

Create an option set for `predict`

using zero initial conditions.

opt = predictOptions('InitialCondition','z');

### Use Historical Data to Specify Initial Conditions for Model Prediction

Load a two-input, one-output dataset.

load iddata7 z7

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`

.

IO = struct('Input',zA.InputData,'Output',zA.OutputData); opt = predictOptions('InitialCondition',IO);

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`

.

To understand how the past data is mapped to the initial states of the model, see Understand Use of Historical Data for Model Prediction.

## Input Arguments

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`predictOptions('InitialCondition','z')`

specifies
zero initial conditions for the measured input-output data.

`InitialCondition`

— Handling of initial conditions

`'e'`

(default) | `'z'`

| `'d'`

| column vector | matrix | `initialCondition`

object | object array | structure | `idpar`

object `x0Obj`

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.`initialCondition`

object —`initialCondition`

object that represents a model of the free response of the system to initial conditions. For multiexperiment data, specify a 1-by-*N*array of objects, where_{e}*N*is the number of experiments._{e}Use this option for linear models 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 an example, see Use Historical Data to Specify Initial Conditions for Model Prediction.

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.

`InputOffset`

— Input signal offset

`[]`

(default) | column vector | matrix

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*i*th 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*i*th input signal of the*j*th experiment before prediction.If you specify a column vector of length

*Nu*, then the offset value`InputOffset(i)`

is subtracted from the*i*th input signal of all the experiments.

`OutputOffset`

— Output signal offset

`[]`

(default) | column vector | matrix

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*i*th 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*i*th output signal of the*j*th experiment before prediction.If you specify a column vector of length

*Ny*, then the offset value`OutputOffset(i)`

is subtracted from the*i*th 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.

`InputInterSample`

— Input interpolation method

`'auto'`

(default) | string | character array

Input interpolation method, specified as:

`'auto'`

,`'foh'`

,`'zoh'`

, or`'bl'`

for continuous-time linear models`'auto'`

,`'foh'`

, or`'zoh'`

for continuous-time nonlinear grey-box models`'auto'`

,`'foh'`

,`'zoh'`

,`'cubic'`

,`'makima'`

,`'pchip'`

, or`'spline'`

for continuous-time neural state-space models

`InputInterSample`

applies only to continuous-time models. If
`InputInterSample`

is `'auto'`

, the software
automatically picks the same input interpolation method as that used for model
estimation.

For information on the interpolation methods, see `nssTrainingADAM`

and `compareOptions`

.

## Output Arguments

`opt`

— Option set for `predict`

`predictOptions`

option set

Option set for `predict`

, returned as a `predictOptions`

option set.

## Version History

**Introduced in R2012a**

## See Also

`predict`

| `absorbDelay`

| `idpar`

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