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predictOptions

Option set for predict

Syntax

opt = predictOptions
opt = predictOptions(Name,Value)

Description

opt = predictOptions creates the default options set for predict.

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

'InitialCondition'

Specify the handling of initial conditions.

InitialCondition takes one of the following:

  • 'z' — Zero initial conditions.

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

  • 'd' — Similar to 'e', but absorbs nonzero delays into the model coefficients.

  • x0 — Numerical column vector denoting initial states. For multi-experiment data, use a matrix with Ne columns, where Ne is the number of experiments. Use this option only for state-space and nonlinear models.

  • io — Structure with the following fields:

    • Input

    • Output

    Use the Input and Output fields to specify the history for a time interval. This interval must start before the start time of the data used by predict. In case the data used by predict is a time series model, specify Input as []. Use a row vector to denote a constant signal value. The number of columns in Input and Output must always equal the number of input and output channels, respectively. For multi-experiment data, specify io as a struct array of Ne elements, where Ne is the number of experiments.

  • x0obj — Specification object created using idpar. Use this object for discrete-time state-space models only. Use x0obj to impose constraints on the initial states by fixing their value or specifying minimum/maximum bounds.

For an idnlgrey model, InitialCondition can also be one of the following:

  • 'fixed'sys.InitialState determines the values of the initial states, but all the states are considered fixed for estimation.

  • 'model'sys.InitialState determines the values of the initial states, which states to estimate and their minimum/maximum values.

Default: 'e'

'InputOffset'

Input signal offset.

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 before the input is used to simulate the model.

Default: []

'OutputOffset'

Output signal offset.

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.

Default: []

'OutputWeight'

Weight of output for initial condition estimation.

OutputWeight takes one of the following:

  • [] — No weighting is used. This option 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 predict.

Examples

expand all

Specify Options for Model Prediction

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

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

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

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

See Also

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