peOptions

Option set for pe

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Syntax

opt = peOptions
opt = peOptions(Name,Value)

Description

opt = peOptions creates the default options set for pe.

opt = peOptions(Name,Value) creates an option 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 quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'InitialCondition'

Handling of initial conditions.

Specify InitialCondition as one of the following:

  • 'z' — Zero initial conditions.

  • 'e' — Estimate initial conditions such that the prediction error for observed output is minimized.

    For nonlinear grey-box models, only those initial states i that are designated as free in the model (sys.InitialStates(i).Fixed = false) are estimated. To estimate all the states of the model, first specify all the Nx states of the idnlgrey model sys as free.

    for i = 1:Nx
sys.InitialStates(i).Fixed = false;
end

    Similarly, to fix all the initial states to values specified in sys.InitialStates, first specify all the states as fixed in the sys.InitialStates property of the nonlinear grey-box model.

  • 'd' — Similar to 'e', but absorbs nonzero delays into the model coefficients. The delays are first converted to explicit model states, and the initial values of those states are also estimated and returned.

    Use this option for linear models only.

  • Vector or Matrix — Initial guess for state values, specified as a numerical column vector of length equal to the number of states. For multi-experiment data, specify a matrix with Ne columns, where Ne is the number of experiments. Otherwise, use a column vector to specify the same initial conditions for all experiments. Use this option for state-space (idss and idgrey) and nonlinear models (idnlarx, idnlhw, and idnlgrey) only.

  • initialCondition object — initialCondition object that represents a model of the free response of the system to initial conditions. For multiexperiment data, specify a 1-by-Ne array of objects, where Ne is the number of experiments.

    Use this option for linear models only.

  • Structure with the following fields, which contain the historical input and output values for a time interval immediately before the start time of the data used by pe:

    FieldDescription
    InputInput history, specified as a matrix with Nu columns, where Nu is the number of input channels. For time series models, use []. The number of rows must be greater than or equal to the model order.
    OutputOutput history, specified as a matrix with Ny columns, where Ny is the number of output channels. The number of rows must be greater than or equal to the model order.

    For multi-experiment data, configure the initial conditions separately for each experiment by specifying InitialCondition as a structure array with Ne elements. To specify the same initial conditions for all experiments, use a single structure.

    The software uses data2state to map the historical data to states. If your model is not idss, idgrey, idnlgrey, or idnlarx, the software first converts the model to its state-space representation and then maps the data to states. If conversion of your model to idss is not possible, the estimated states are returned empty.

  • x0obj — Specification object created using idpar. Use this object for discrete-time state-space (idss and idgrey) and nonlinear grey-box (idnlgrey) models only. Use x0obj to impose constraints on the initial states by fixing their value or specifying minimum or maximum bounds.

Default: 'e'

'InputOffset'

Removes offset from time domain input data during prediction-error calculation.

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

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.

Specify input offset for only time domain data.

Default: []

'OutputOffset'

Removes offset from time domain output data during prediction-error calculation.

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

In case of 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.

Specify output offset for only time domain data.

Default: []

'OutputWeight'

Weight of output for initial condition estimation.

OutputWeight takes one of the following:

  • [] — No weighting is used. This value is the same as using eye(Ny) for the output weight, where Ny is the number of outputs.

  • 'noise' — Inverse of the noise variance stored with the model.

  • matrix — A positive, semidefinite matrix of dimension Ny-by-Ny, where Ny is the number of outputs.

Default: []

Output Arguments

opt

Option set containing the specified options for pe.

Examples

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Open Live Script
opt = peOptions;
Open Live Script

Create an options set for pe using zero initial conditions, and set the input offset to 5. 

opt = peOptions('InitialCondition','z','InputOffset',5);

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

opt = peOptions;
opt.InitialCondition = 'z';
opt.InputOffset = 5;

See Also

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Introduced in R2012a

System Identification Toolbox Documentation

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