Regressor expressions and numerical values in nonlinear ARX model

`Rs = getreg(model)`

Rs = getreg(model,subset)

Rm = getreg(model,subset,data)

Rm = getreg(model,subset,data,init)

`Rs = getreg(model)`

returns expressions
for computing regressors in the nonlinear ARX model. `Rs`

is
a cell array of strings. `model`

is an `idnlarx`

object.

`Rs = getreg(model,subset)`

returns regressor
expressions for a specified subset of regressors. `subset`

is
a string.

`Rm = getreg(model,subset,data)`

returns
regressor values as a matrix for a specified subset of regressors.

`Rm = getreg(model,subset,data,init)`

returns
regressor values as matrices for a specified subset of regressors.
The first `N`

rows of each regressor matrix depend
on the initial states `init`

, where `N`

is
the maximum delay in the regressors (see `getDelayInfo`

).
For multiple-output models, `Rm`

is a cell array
of cell arrays.

`data`

`iddata`

object containing measured data.`init`

Initial conditions of your data:

`'z'`

(default) specifies zero initial state.Real column vector containing the initial state values. input and output data values at a time instant before the first sample in

`data`

. To create the initial state vector from the input-output data, use the`data2state`

command. For multiple-experiment data, this is a matrix where each column specifies the initial state of the model corresponding to that experiment.`iddata`

object containing input and output samples at time instants before to the first sample in`data`

. When the`iddata`

object contains more samples than the maximum delay in the model, only the most recent samples are used. The minimum number of samples required is equal to`max(getDelayInfo(model))`

.

`model`

`iddata`

object representing nonlinear ARX model.`subset`

String that represents a subset of all regressors:

(Default)

`'all'`

— All regressors.`'custom'`

—Only custom regressors.`'input'`

—Only standard regressors computed from input data.`'linear'`

—Only regressors not used in the nonlinear block.`'nonlinear'`

—Only regressors used in the nonlinear block.**Note:**You can use`'nl'`

as an abbreviation of`'nonlinear'`

.`'output'`

—Only regressors computed from output data.`'standard'`

—Only standard regressors (excluding any custom regressors).

`Rm`

Matrix of regressor values for all or a specified subset of regressors. Each matrix in

`Rm`

contains as many rows as there are data samples. For a model with`ny`

outputs,`Rm`

is an`ny`

-by-1 cell array of matrices. When`data`

contains multiple experiments,`Rm`

is a cell array where each element corresponds to a matrix of regressor values for an experiment.`Rs`

Regressor expressions represented as a cell array of strings. For a model with

`ny`

outputs,`Rs`

is an`ny`

-by-1 cell array of cell arrays of strings. For example, the expression`'u1(t-2)'`

computes the regressor by delaying the input signal`u1`

by two time samples. Similarly, the expression`'y2(t-1)'`

computes the regressor by delaying the output signal`y2`

by one time sample.The order of regressors in

`Rs`

corresponds to regressor indices in the`idnlarx`

object property`model.NonlinearRegressors`

.

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