Option set for arx
opt = arxOptions
opt = arxOptions(Name,Value)
creates
the default options set for opt
= arxOptionsarx
.
creates
an option set with the options specified by one or more opt
= arxOptions(Name,Value
)Name,Value
pair
arguments.
Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.
'InitialCondition'
— Initial condition'auto'
(default)  'zero'
 'estimate'
 'backcast'
Specify how initial conditions are handled during estimation.
InitialCondition
requires one of the following
values:
'zero'
— The initial conditions
are set to zero.
'estimate'
— The initial
conditions are treated as independent estimation parameters.
'backcast'
— The initial
conditions are estimated using the best least squares fit.
'auto'
— The software chooses
the method to handle initial conditions based on the estimation data.
'Focus'
— Estimation focus'prediction'
(default)  'simulation'
 'stability'
 vector  matrix  linear systemEstimation focus that defines how the errors e between the measured and the modeled outputs are weighed at specific frequencies during the minimization of the prediction error, specified as one of the following:
'prediction'
— Automatically
calculates the weighting function as a product of the input spectrum
and the inverse of the noise spectrum. The weighting function minimizes
the onestepahead prediction. This approach typically favors fitting
small time intervals (higher frequency range). From a statisticalvariance
point of view, this weighting function is optimal. However, this method
neglects the approximation aspects (bias) of the fit.
This option focuses on producing a good predictor and does not
enforce model stability. Use 'stability'
when you
want to ensure a stable model.
'simulation'
— Estimates
the model using the frequency weighting of the transfer function that
is given by the input spectrum. Typically, this method favors the
frequency range where the input spectrum has the most power. This
method provides a stable model.
'stability'
— Same as 'prediction'
,
but with model stability enforced.
Passbands — Row vector or matrix containing frequency values that define desired passbands. For example:
[wl,wh] [w1l,w1h;w2l,w2h;w3l,w3h;...]
where wl
and wh
represent
lower and upper limits of a passband. For a matrix with several rows
defining frequency passbands, the algorithm uses union of frequency
ranges to define the estimation passband.
Passbands are expressed in rad/TimeUnit
for
timedomain data and in FrequencyUnit
for frequencydomain
data, where TimeUnit
and FrequencyUnit
are
the time and frequency units of the estimation data.
SISO filter — Specify a SISO linear filter in one of the following ways:
A singleinputsingleoutput (SISO) linear system
{A,B,C,D}
format, which specifies
the statespace matrices of the filter
{numerator, denominator}
format,
which specifies the numerator and denominator of the filter transfer
function
This option calculates the weighting function as a product of
the filter and the input spectrum to estimate the transfer function.
To obtain a good model fit for a specific frequency range, you must
choose the filter with a passband in this range. The estimation result
is the same if you first prefilter the data using idfilt
.
Weighting vector — For frequencydomain 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.
'EstCovar'
— Control whether to generate parameter covariance datatrue
(default)  false
Controls whether parameter covariance data is generated, specified
as true
or false
.
If EstCovar
is true
,
then use getcov
to fetch the
covariance matrix from the estimated model.
'Display'
— Specify whether to display the estimation progress'off'
(default)  'on'
Specify whether to display the estimation progress, specified as one of the following strings:
'on'
— Information on model
structure and estimation results are displayed in a progressviewer
window.
'off'
— No progress or results
information is displayed.
'InputOffset'
— Removal of offset from timedomain input data during estimation[]
(default)  vector of positive integers  matrixRemoval of offset from timedomain input data during estimation,
specified as the commaseparated pair consisting of 'InputOffset'
and
one of the following:
A column vector of positive integers of length Nu, where Nu is the number of inputs.
[]
— indicates no offset
NubyNe matrix
— For multiexperiment data, specify InputOffset
as
an NubyNe 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 timedomain output data during estimation[]
(default)  vector  matrixRemoval of offset from time domain output data during estimation,
specified as the commaseparated pair consisting of 'OutputOffset'
and
one of the following:
A column vector of length Ny, where Ny is the number of outputs.
[]
— indicates no offset
NybyNe matrix
— For multiexperiment data, specify OutputOffset
as
a NybyNe 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 multioutput estimation.
Specify OutputWeight
as a positive semidefinite,
symmetric matrix (W
). The software minimizes the
trace of the weighted prediction error matrix trace(E'*E*W)
. E
is
the matrix of prediction errors, with one column for each output,
and W
is the positive semidefinite, symmetric matrix
of size equal to the number of outputs. Use W
to
specify the relative importance of outputs in multipleinput, multipleoutput
models, or the reliability of corresponding data.
This option is relevant only for multioutput models.
'Regularization'
— Options for regularized estimation of ARX model parameters. For more information on regularization, see Regularized Estimates of Model Parameters.
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
— The nominal value
towards which the free parameters are pulled during estimation.
The default value of zero implies that the parameter values
are pulled towards zero. If you are refining a model, you can set
the value to 'model'
to pull the parameters towards
the parameter values of the initial model. The initial parameter values
must be finite for this setting to work.
Default: 0
Use arxRegul
to automatically
determine Lambda and R values.
'Advanced'
— Advanced
is a structure with the following
fields:
MaxSize
— Specifies the
maximum number of elements in a segment when inputoutput 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 rightmost pole to test the stability of continuoustime models.
A model is considered stable when its rightmost pole is to the left
of s
.
Default: 0
z
— Specifies the maximum
distance of all poles from the origin to test stability of discretetime
models. A model is considered stable if all poles are within the distance z
from
the origin.
Default: 1+sqrt(eps)

Option set containing the specified options for 
opt = arxOptions;
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';