arxRegulOptions

Option set for arxRegul

Syntax

opt = arxRegulOptions

opt = arxRegulOptions(Name,Value)

Description

opt = arxRegulOptions creates a default option set for arxRegul.

opt = arxRegulOptions(Name,Value) creates an options set with the options specified by one or more name-value pair arguments.

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, where Nu is the number of inputs. For multi-experiment data, specify a Nu-by-Ne matrix, where Ne is the number of experiments. The offset value InputOffset(i,j) is subtracted from the i^th input signal of the j^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, where Ny is the number of outputs. For multi-experiment data, specify a Ny-by-Ne matrix, where Ne is the number of experiments. The offset value OputOffset(i,j) is subtracted from the i^th output signal of the j^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 R2014a

R2018a: Renaming of Estimation and Analysis Options

The names of some estimation and analysis options were changed in R2018a. Prior names still work.