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ssregestOptions

Option set for ssregest

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Syntax

options = ssregestOptions
options = ssregestOptions(Name,Value)

Description

options = ssregestOptions creates a default option set for ssregest.

example

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

example

Examples

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Open Live Script
options = ssregestOptions;
Open Live Script

Create an option 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');

Alternatively, use dot notation to set the values of opt. 

opt = ssregestOptions;
opt.InitialState = 'zero';
opt.Display = 'on';

Input Arguments

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

Handling of initial states during estimation, specified as one of the following values:

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

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

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.

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

For more information, see [1].

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, the reduction algorithm of ssregest 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 (Control System Toolbox) 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

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.

Weighting 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.

Option to generate parameter covariance data, specified as true or false.

If EstimateCovariance is true, then use getcov to fetch the covariance matrix from the estimated model.

Option 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.

Input-channel intersample behavior for transformations between discrete time and continuous time, specified as 'auto', 'zoh','foh', or 'bl'.

The definitions of the three behavior values are as follows:

  • 'zoh' — Zero-order hold maintains a piecewise-constant input signal between samples.

  • 'foh' — First-order hold maintains a piecewise-linear input signal between samples.

  • 'bl' — Band-limited behavior specifies that the continuous-time input signal has zero power above the Nyquist frequency.

iddata objects have a similar property, data.InterSample, that contains the same behavior value options. When the InputInterSample value is 'auto' and the estimation data is in an iddata object data, the software uses the data.InterSample value. When the estimation data is instead contained in a timetable or a matrix pair, with the 'auto' option, the software uses 'zoh'.

The software applies the same option value to all channels and all experiments.

Removal of offset from time-domain input data during estimation, specified as 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.

Removal of offset from time-domain output data during estimation, specified as 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.

Weight 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.

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

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Estimation options for ssregest, returned as an ssregestoptions option 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

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See Also

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