# peOptions

Option set for `pe`

## Description

Use `peOptions`

to create option sets when using the function
`pe`

.

## Creation

### Description

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

= peOptions(`Name,Value`

)`Name,Value`

arguments.

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

in quotes. For example, ```
opt =
peOptions('InitialCondition','z','InputOffset',5)
```

creates a
`peOptions`

object and specifies the
`InitialCondition`

as zero.

## Properties

`InitialCondition`

— Initial condition handling

`'e'`

(default) | `'z'`

| `'d'`

| vector | matrix | `initialCondition`

object | `x0obj`

Handling of initial conditions.

Specify `InitialCondition`

as one of the
following:

`'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 by

`pe`

: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.

`InputOffset`

— Removes offset from time domain input data

`[]`

(default) | column vector | matrix

Removes offset from time domain input data during prediction-error calculation.

Specify as a column vector of length *Nu*, where
*Nu* is the number of inputs.

For multi-experiment data, specify `InputOffset`

as
an *Nu*-by-*Ne* matrix.
*Nu* is the number of inputs, and
*Ne* is the number of experiments.

Each entry specified by `InputOffset`

is subtracted
from the corresponding input data.

Specify input offset for only time domain data.

`OutputOffset`

— Removes offset from time domain output data

`[]`

(default) | column vector | matrix

Removes offset from time domain output data during prediction-error calculation.

Specify as a column vector of length *Ny*, where
*Ny* is the number of outputs.

In case of multi-experiment data, specify
`OutputOffset`

as a
*Ny*-by-*Ne* matrix.
*Ny* is the number of outputs, and
*Ne* is the number of experiments.

Each entry specified by `OutputOffset`

is
subtracted from the corresponding output data.

Specify output offset for only time domain data.

`OutputWeight`

— Weight of output

`[]`

(default) | `'noise'`

| matrix

Weight of output for initial condition estimation.

`OutputWeight`

takes one of the following:

`[]`

— No weighting is used. This value is the same as using`eye(Ny)`

for the output weight, where*Ny*is the number of outputs.`'noise'`

— Inverse of the noise variance stored with the model.matrix — A positive, semidefinite matrix of dimension

*Ny*-by-*Ny*, where*Ny*is the number of outputs.

`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`

.

## Examples

### Create Default Options Set for Prediction-Error Calculation

opt = peOptions;

### Specify Options for Prediction-Error Calculation

Create an options set for `pe`

using zero initial conditions, and set the input offset to 5.

opt = peOptions('InitialCondition','z','InputOffset',5);

Alternatively, use dot notation to set the values of `opt`

.

```
opt = peOptions;
opt.InitialCondition = 'z';
opt.InputOffset = 5;
```

## Version History

**Introduced in R2012a**

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