Contents

arxRegulOptions

Option set for arxRegul

Syntax

  • opt = arxRegulOptions example
  • opt = arxRegulOptions(Name,Value) example

Description

example

opt = arxRegulOptions creates a default options set for arxRegul.

example

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

Input Arguments

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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 single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: option = arxRegulOptions('RegulKernel', 'DC') specifies 'DC' as the regularization kernel.

'RegulKernel' — Regularization kernel'TC' (default) | 'SE' | 'SS' | 'HF' | 'DI' | 'DC'

Regularization kernel, specified as one of the following strings:

  • '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 the estimation data[] (default) | vector | matrix

Input signal offset level 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 ith input signal of the jth 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 ith output signal of the jth experiment.

  • [] — No offsets.

The specified values are subtracted from the output signals before using them for estimation.

Data Types: double

'Advanced' — Advanced estimation optionsstructure

Advanced options for regularized estimation, specified as a structure with the following fields:

  • MaxSize — Maximum allowable size of Jacobian matrices formed during estimation. Specify a large positive number.

    Default: 250e3

  • SearchMethod — Search method for estimating regularization parameters. Must be one of the following strings:

    • 'fmincon': Trust-region-reflective constrained minimizer. Requires Optimization Toolbox™ software. In general, 'fmincon' is better than 'gn' for handling bounds on regularization parameters that are imposed automatically during estimation.

    • 'gn': Quasi-Newton line search

    Default: 'fmincon'

    If you do not have the Optimization Toolbox software, the default is 'gn'.

Data Types: struct

Output Arguments

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opt — Regularization optionsarxRegulOptions 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.

See Also

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