arxOptions
Option set for arx
Description
Use an arxOptions
object to specify options for estimating
parameters of ARX, ARIX, AR, or ARI models through the arx
function. You can specify options such as the handling of initial conditions
or the ability to display estimation progress.
Creation
Properties
InitialCondition
— Handling of initial conditions
'auto'
(default) | 'zero'
| 'estimate'
Handling of initial conditions during estimation using frequency-domain data,
specified as the comma-separated pair consisting of
'InitialCondition'
and one of the following values:
'zero'
— The initial conditions are set to zero.'estimate'
— The initial conditions are treated as independent estimation parameters.'auto'
— The software chooses the method to handle initial conditions based on the estimation data.
Focus
— Error to be minimized
'prediction'
(default) | 'simulation'
Error to be minimized in the loss function during estimation,
specified as the comma-separated pair consisting of 'Focus'
and
one of the following values:
'prediction'
— The one-step ahead prediction error between measured and predicted outputs is minimized during estimation. As a result, the estimation focuses on producing a good predictor model.'simulation'
— The simulation error between measured and simulated outputs is minimized during estimation. As a result, the estimation focuses on making a good fit for simulation of model response with the current inputs.
The Focus
option can be interpreted as a
weighting filter in the loss function. For more information, see Loss Function and Model Quality Metrics.
WeightingFilter
— Weighting prefilter
[]
(default) | vector | matrix | cell array | linear system
Weighting prefilter applied to the loss function to be minimized
during estimation. To understand the effect of WeightingFilter
on
the loss function, see Loss Function and Model Quality Metrics.
Specify WeightingFilter
as one of the following
values:
[]
— No weighting prefilter is used.Passbands — Specify a row vector or matrix containing frequency values that define desired passbands. You select a frequency band where the fit between estimated model and estimation data is optimized. For example,
[wl,wh]
wherewl
andwh
represent lower and upper limits of a passband. For a matrix with several rows defining frequency passbands,[w1l,w1h;w2l,w2h;w3l,w3h;...]
, the estimation algorithm uses the union of the frequency ranges to define the estimation passband.Passbands are expressed in
rad/TimeUnit
for time-domain data and inFrequencyUnit
for frequency-domain data, whereTimeUnit
andFrequencyUnit
are the time and frequency units of the estimation data.SISO filter — Specify a single-input-single-output (SISO) linear filter in one of the following ways:
A SISO LTI model
{A,B,C,D}
format, which specifies the state-space matrices of a filter with the same sample time as estimation data.{numerator,denominator}
format, which specifies the numerator and denominator of the filter as a transfer function with same sample time as estimation data.This option calculates the weighting function as a product of the filter and the input spectrum to estimate the transfer function.
Weighting vector — Applicable for frequency-domain data only. Specify a column vector of weights. This vector must have the same length as the frequency vector of the data set,
Data.Frequency
. Each input and output response in the data is multiplied by the corresponding weight at that frequency.
EnforceStability
— Control whether to enforce stability of model
false
(default) | true
Control whether to enforce stability of estimated model, specified
as the comma-separated pair consisting of 'EnforceStability'
and
either true
or false
.
This option is not available for multi-output models with a non-diagonal A polynomial array.
Data Types: logical
EstimateCovariance
— Option to generate parameter covariance data
true
(default) | false
Option to generate parameter covariance data, specified as true
or
false
.
If EstimateCovariance
is true
, then use
getcov
to fetch the covariance matrix
from the estimated model.
Display
— Option to display estimation progress
'off'
(default) | 'on'
Option to display the estimation progress, specified as one of the following values:
'on'
— Information on model structure and estimation results are displayed in a progress-viewer window.'off'
— No progress or results information is displayed.
InputOffset
— Removal of offset from time-domain input data during estimation
[]
(default) | vector of positive integers | matrix
Removal of offset from time-domain input data during estimation, specified as one of the following:
A column vector of positive integers of length Nu, where Nu is the number of inputs.
[]
— Indicates no offset.Nu-by-Ne matrix — 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.
OutputOffset
— Removal of offset from time-domain output data during estimation
[]
(default) | vector | matrix
Removal of offset from time-domain output data during estimation, specified as one of the following:
A column vector of length Ny, where Ny is the number of outputs.
[]
— Indicates no offset.Ny-by-Ne matrix — For 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.
OutputWeight
— Weight of prediction errors in multi-output estimation
[]
(default) | positive semidefinite, symmetric matrix
Weight of prediction errors in multi-output estimation, specified as one of the following values:
Positive semidefinite, symmetric matrix (
W
). The software minimizes the trace of the weighted prediction error matrixtrace(E'*E*W/N)
where:E
is the matrix of prediction errors, with one column for each output, andW
is the positive semidefinite, symmetric matrix of size equal to the number of outputs. UseW
to specify the relative importance of outputs in multiple-output models, or the reliability of corresponding data.N
is the number of data samples.
[]
— No weighting is used. Specifying as[]
is the same aseye(Ny)
, whereNy
is the number of outputs.
This option is relevant only for multi-output models.
Regularization
— Options for regularized estimation of model parameters
[]
(default) | positive semidefinite, symmetric matrix
Options for regularized estimation of model parameters, specified as a structure with the following fields:
Lambda
— Constant that determines the bias versus variance tradeoff.Specify a positive scalar to add the regularization term to the estimation cost.
The default value of zero implies no regularization.
Default: 0
R
— Weighting matrix.Specify a positive scalar or a positive definite matrix. The length of the matrix must be equal to the number of free parameters (
np
) of the model. For ARX model,np
= sum(sum([na
nb
]).Default: 1
Nominal
— This option is not used for ARX models.Default: 0
Use arxRegul
to automatically determine Lambda
and R values.
For more information on regularization, see Regularized Estimates of Model Parameters.
Advanced
— Additional advanced options
structure
Additional advanced options, specified as a structure with the following fields:
MaxSize
— Specifies the maximum number of elements in a segment when input-output data is split into segments.MaxSize
must be a positive integer.Default:
250000
StabilityThreshold
— Specifies thresholds for stability tests.StabilityThreshold
is a structure with the following fields:s
— Specifies the location of the right-most pole to test the stability of continuous-time models. A model is considered stable when its right-most pole is to the left ofs
.Default:
0
z
— Specifies the maximum distance of all poles from the origin to test stability of discrete-time models. A model is considered stable if all poles are within the distancez
from the origin.Default:
1+sqrt(eps)
Examples
Create Default Options Set for ARX Estimation
opt = arxOptions;
Specify Options for ARX Estimation
Create an options set for arx
using zero initial conditions for estimation. Set Display
to 'on'
.
opt = arxOptions('InitialCondition','zero','Display','on');
Alternatively, use dot notation to set the values of opt
.
opt = arxOptions; opt.InitialCondition = 'zero'; opt.Display = 'on';
Version History
Introduced in R2012aR2018a: Renaming of Estimation and Analysis Options
The names of some estimation and analysis options were changed in R2018a. Prior names still work.
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