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Options set for impulseest


options = impulseestOptions
options = impulseestOptions(Name,Value)


options = impulseestOptions creates a default options set for impulseest.

options = impulseestOptions(Name,Value) creates an options set with the options specified by one or more Name,Value pair arguments.

Input Arguments

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.


Regularizing kernel, used for regularized estimates 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, see [1].

Regularization is specified as one of the following values:

  • 'TC' — Tuned and correlated kernel

  • 'none' — No regularization is used

  • 'CS' — Cubic spline kernel

  • 'SE' — Squared exponential kernel

  • 'SS' — Stable spline kernel

  • 'HF' — High frequency stable spline kernel

  • 'DI' — Diagonal kernel

  • 'DC' — Diagonal and correlated kernel

Default: 'TC'


Order of the input prewhitening filter. Must be one of the following:

  • 'auto' — Uses a filter of order 10 when RegulKernel is 'none'; otherwise, 0.

  • Nonnegative integer

Use a nonzero value of prewhitening only for unregularized estimation (RegulKernel is 'none').

Default: 'auto'


Input signal offset level of time-domain estimation data. Must be 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.

Default: []


Output signal offset level of time-domain estimation data. Must be 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.

Default: []


Structure, used during regularized estimation, 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, specified as one of the following values:

    • '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.

    • 'gn': Quasi-Newton line search.

    SearchMethod is used only when RegulKernel is not 'none'.

    Default: 'fmincon'

  • AROrder — Order of the AR-part in the model from input to output. Specify as a positive integer.

    An order>0 allows more accurate models of the impulse response in case of feedback and non-white output disturbances.

    Default: 5

  • FeedthroughInSys — Specify whether the impulse response value at zero lag must be attributed to feedthrough in the system (true) or to feedback effects (false). Applies only when you compute the response values for negative lags.

    Default: false

Output Arguments


Option set containing the specified options for impulseest.


collapse all

Create a default options set for impulseest.

options = impulseestOptions;

Specify 'HF' regularizing kernel and order of prewhitening filter for impulseest.

options = impulseestOptions('RegulKernel','HF','PW',5);

Alternatively, use dot notation to specify these options.

options = impulseestOptions;
options.RegulKernel = 'HF';
options.PW = 5;


  • A linear model cannot describe arbitrary input-output offsets. Therefore, before using the data, you must either detrend it or remove the levels using InputOffset and OutputOffset. You can reintroduce the removed data during simulations by using the InputOffset and OutputOffset simulation options. For more information, see simOptions.

  • Estimating the impulse response by specifying either InputOffset, OutputOffset or both is equivalent to detrending the data using getTrend and detrend. For example:

    opt = impulseestOptions('InputOffest',in_off,'OuputOffset',out_off);

    is the same as:

    Tr = getTrend(data),
    Tr.InputOffset = in_off
    TR.OutputOffset = out_off
    dataT = detrend(data,Tr)


[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

Introduced in R2012b

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