peOptions

Option set for pe

Description

Use peOptions to create option sets when using the function pe.

Creation

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 arguments.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes. For example, opt = peOptions('InitialCondition','z','InputOffset',5) creates a peOptions object and specifies the InitialCondition as zero.

Properties

InitialCondition — Initial condition handling

'e' (default) | 'z' | 'd' | vector | matrix | initialCondition object | x0obj

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-N_e array of objects, where N_e 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:

  Field	Description
  Input	Input 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.
  Output	Output 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.

InputOffset — Removes offset from time domain input data

[] (default) | column vector | matrix

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.

OutputOffset — Removes offset from time domain output data

[] (default) | column vector | matrix

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.

OutputWeight — Weight of output

[] (default) | 'noise' | matrix

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.

InputInterSample — Input interpolation method

'auto' (default) | string | character array

Input interpolation method, specified as:

• 'auto', 'foh', 'zoh', or 'bl' for continuous-time linear models

• 'auto', 'foh', or 'zoh' for continuous-time nonlinear grey-box models

• 'auto', 'foh', 'zoh', 'cubic', 'makima', 'pchip', or 'spline' for continuous-time neural state-space models

InputInterSample applies only to continuous-time models. If InputInterSample is 'auto', the software automatically picks the same input interpolation method as that used for model estimation.

For information on the interpolation methods, see nssTrainingADAM and compareOptions.

Examples

Create Default Options Set for Prediction-Error Calculation

opt = peOptions;

Specify Options for Prediction-Error Calculation

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;

Version History

Introduced in R2012a