arxRegulOptions
Option set for arxRegul
Description
Examples
Create Default Options Set for Determining Regularization Constants
opt = arxRegulOptions;
Specify Regularizing Kernel for ARX Model Estimation
opt = arxRegulOptions('RegularizationKernel','DC');
Input Arguments
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: option = arxRegulOptions('RegularizationKernel', 'DC')
specifies
'DC'
as the regularization kernel.
RegularizationKernel
— Regularization kernel
'TC'
(default) | 'SE'
| 'SS'
| 'HF'
| 'DI'
| 'DC'
Regularization kernel, specified as one of the following values:
'TC'
— Tuned and correlated kernel'SE'
— Squared exponential kernel'SS'
— Stable spline kernel'HF'
— High frequency stable spline kernel'DI'
— Diagonal kernel'DC'
— Diagonal and correlated kernel
The specified kernel is used for regularized estimation of impulse response for all input-output channels. Regularization reduces variance of estimated model coefficients and produces a smoother response by trading variance for bias.
For more information about these choices, see [1].
Data Types: char
InputOffset
— Offset levels present in the input signals of estimation data
[]
(default) | vector | matrix
Offset levels present in the input signals of time-domain estimation data, specified as one of the following:
An
Nu
-element column vector, whereNu
is the number of inputs. For multi-experiment data, specify aNu
-by-Ne
matrix, whereNe
is the number of experiments. The offset valueInputOffset(i,j)
is subtracted from thei
th input signal of thej
th experiment.[]
— No offsets.
Data Types: double
OutputOffset
— Output signal offset levels
[]
(default) | vector | matrix
Output signal offset level of time-domain estimation data, specified as one of the following:
An
Ny
-element column vector, whereNy
is the number of outputs. For multi-experiment data, specify aNy
-by-Ne
matrix, whereNe
is the number of experiments. The offset valueOputOffset(i,j)
is subtracted from thei
th output signal of thej
th experiment.[]
— No offsets.
The specified values are subtracted from the output signals before using them for estimation.
Data Types: double
Advanced
— Advanced estimation options
structure
Advanced options for regularized estimation, specified as a structure with the following fields:
MaxSize
— Maximum allowable size of Jacobian matrices formed during estimation, specified as a large positive number.Default:
250e3
SearchMethod
— Search method for estimating regularization parameters, specified as one of the following values:'gn'
: Quasi-Newton line search.'fmincon'
: Trust-region-reflective constrained minimizer. In general,'fmincon'
is better than'gn'
for handling bounds on regularization parameters that are imposed automatically during estimation.
Default:
'fmincon'
Output Arguments
opt
— Regularization options
arxRegulOptions
options set
Regularization options, returned as an arxRegulOptions
options
set.
References
[1] T. Chen, H. Ohlsson, and L. Ljung. “On the Estimation of Transfer Functions, Regularizations and Gaussian Processes - Revisited”, Automatica, Volume 48, August 2012.
Version History
Introduced in R2014aR2018a: 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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