Option set for arx
opt = arxOptions
opt = arxOptions(Name,Value)
opt = arxOptionsarx.
opt = arxOptions(Name,Value)Name,Value pair
arguments.
Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.
'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 systemWeighting 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] where wl and wh 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 in FrequencyUnit for frequency-domain
data, where TimeUnit and FrequencyUnit 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 modelfalse (default) | trueControl 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' — Control whether to generate parameter covariance datatrue (default) | falseControls whether parameter covariance data is generated, specified as
true or false.
If EstimateCovariance 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 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 | matrixRemoval of offset from time-domain input data during estimation,
specified as the comma-separated 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.
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 | matrixRemoval of offset from time-domain output data during estimation,
specified as the comma-separated 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.
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 matrixWeight 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
matrix trace(E'*E*W/N) where:
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 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 as eye(Ny),
where Ny 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 matrixOptions 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 optionsAdditional 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
of s.
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 distance z from
the origin.
Default: 1+sqrt(eps)
opt — Options set for arxarxOptions option setOption set for arx, returned
as an arxOptions option set.
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';
The names of some estimation and analysis options were changed in R2018a. Prior names still work. For details, see the R2018a release note Renaming of Estimation and Analysis Options.
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