ssregestOptions

Options set for ssregest

Syntax

  • options = ssregestOptions example
  • options = ssregestOptions(Name,Value) example

Description

example

options = ssregestOptions creates a default options set for ssregest.

example

options = ssregestOptions(Name,Value) specifies additional options using one or more Name,Value pair arguments.

Examples

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Specify Options for State-Space Estimation Using Reduction of Regularized ARX Model

Create an options set for ssregest that fixes the value of the initial states to 'zero'. Also, set the Display to 'on'.

opt = ssregestOptions('InitialState','zero','Display','on');

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: opt = ssregestOptions('InitialState','zero') fixes the value of the initial states to zero.

'InitialState' — Specify handling of initial states during estimation'estimate' (default) | 'zero'

Specify handling of initial states during estimation, specified as one of the following strings:

  • 'zero' — The initial state is set to zero.

  • 'estimate' — The initial state is treated as an independent estimation parameter.

'ARXOrder' — ARX model orders'auto' (default) | matrix of nonnegative integers

ARX model orders, specified as a matrix of nonnegative integers [na nb nk]. The max(ARXOrder)+1 must be greater than the desired state-space model order (number of states). If you specify a value, it is recommended that you use a large value for nb order. To learn more about ARX model orders, see arx.

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

Regularizing kernel used for regularized estimates of the underlying ARX model, 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

For more information, see [1].

'Reduction' — Options for model order reductionstructure

Options for model order reduction, specified as a structure with the following fields:

  • StateElimMethod — State elimination method. Specifies how to eliminate the weakly coupled states (states with smallest Hankel singular values). Specified as one of the following values:

    'MatchDC'Discards the specified states and alters the remaining states to preserve the DC gain.
    'Truncate'Discards the specified states without altering the remaining states. This method tends to product a better approximation in the frequency domain, but the DC gains are not guaranteed to match.

    Default: 'Truncate'

  • AbsTol, RelTol — Absolute and relative error tolerance for stable/unstable decomposition. Positive scalar values. For an input model G with unstable poles, balred first extracts the stable dynamics by computing the stable/unstable decomposition G → GS + GU. The AbsTol and RelTol tolerances control the accuracy of this decomposition by ensuring that the frequency responses of G and GS + GU differ by no more than AbsTol + RelTol*abs(G). Increasing these tolerances helps separate nearby stable and unstable modes at the expense of accuracy. See stabsep for more information.

    Default: AbsTol = 0; RelTol = 1e-8

  • Offset — Offset for the stable/unstable boundary. Positive scalar value. In the stable/unstable decomposition, the stable term includes only poles satisfying

    • Re(s) < -Offset * max(1,|Im(s)|) (Continuous time)

    • |z| < 1 - Offset (Discrete time)

      Increase the value of Offset to treat poles close to the stability boundary as unstable.

      Default: 1e-8

'Focus' — Estimation focus'prediction' (default) | 'simulation' | vector | matrix | linear system

Estimation 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 model. The weighting function minimizes the one-step-ahead prediction, which typically favors fitting small time intervals (higher frequency range). From a statistical-variance point of view, this weighting function is optimal. This option focuses on producing a good predictor.

  • '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.

  • 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 upper and lower 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.

  • SISO filter — Enter any SISO linear filter in any of the following ways:

    • A single-input-single-output (SISO) linear system.

    • The {A,B,C,D} format, which specifies the state-space matrices of the filter.

    • The {numerator, denominator} format, which specifies the numerator and denominator of the filter transfer function

      This format 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 frequency-domain data only, enter a column vector of weights for 'Focus'. This vector must have the same size as length of 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.

Higher weighting at specific frequencies emphasizes the requirement for a good fit at these frequencies.

'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:

Display requires one of the following strings:

  • 'on' — Information on model structure and estimation results are displayed in a progress-viewer window

  • 'off' — No progress or results information is displayed

'InputOffset' — Remove offset from time-domain input data during estimation[] (default) | vector of positive integers

Removes offset from time-domain input data during estimation, specified as a vector of positive integers.

Specify as a column vector of length Nu, where Nu is the number of inputs.

Use [] to indicate no offset.

For multiexperiment data, specify InputOffset as a 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' — Remove offset from time-domain output data during estimation[] (default) | vector

Removes offset from time domain output data during estimation, specified as a vector of positive integers or [].

Specify as a column vector of length Ny, where Ny is the number of outputs.

Use [] to indicate no offset.

For multiexperiment 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' — Criterion used during minimization[] (default) | 'noise' | positive semidefinite symmetric matrix

Specifies criterion used during minimization, specified as one of the following:

  • 'noise' — Minimize det(E'*E), where E represents the prediction error. This choice is optimal in a statistical sense and leads to the maximum likelihood estimates in case no data is available about the variance of the noise. This option uses the inverse of the estimated noise variance as the weighting function.

  • Positive semidefinite symmetric matrix (W) — Minimize 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. 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-input, multiple-output models, or the reliability of corresponding data.

    This option is relevant only for multi-input, multi-output models.

  • [] — The software chooses between the 'noise' or using the identity matrix for W.

'Advanced' — Maximum allowable size of Jacobian matrices and choice of search methodstructure

Maximum allowable size of Jacobian matrices and choice of search method, 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 strings:

    • '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. Requires Optimization Toolbox™ software.

    • 'gn': Quasi-Newton line search

    Default: 'fmincon'

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

Output Arguments

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options — Options set for ssregestssregestOptions options set

Estimation options for ssregest, returned as an ssregestOptions 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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