Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

predictOptions

Option set for predict

Syntax

opt = predictOptions
opt = predictOptions(Name,Value)

Description

example

opt = predictOptions creates the default option set for predict. Use dot notation to modify this option set. Any options that you do not modify retain their default values.

example

opt = predictOptions(Name,Value) creates an option set with options specified by one or more Name,Value pair arguments.

Examples

collapse all

Create a default option set for model prediction.

opt = predictOptions;

Specify the output offsets for a two-output model as 2 and 5, respectively.

opt.OutputOffset = [2;5];

The software subtracts the offset value OutputOffset(i) from the i th output signal before using the output to predict the model response. The software then adds back these offsets to the predicted response to give the final response.

Create an option set for predict using zero initial conditions.

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

Load a two-input, one-output dataset.

load iddata7 z7

Identify a fifth-order state-space model using the data.

sys = n4sid(z7,5);

Split the dataset into two parts.

zA = z7(1:15);
zB = z7(16:end);

Suppose that you want to compute the 10-step-ahead prediction of the response of the identified system for data zB. For initial conditions, use the signal values in zA as the historical record. That is, the input and output values for the time immediately preceding data in zB.

IO = struct('Input',zA.InputData,'Output',zA.OutputData);
opt = predictOptions('InitialCondition',IO);

Generate the 10-step-ahead prediction for data zB using the specified initial conditions.

[yp,x0,Predictor] = predict(sys,zB,10,opt);

yp is the predicted model response, x0 are the initial states corresponding to the predictor model Predictor. You can simulate Predictor using x0 as initial conditions to reproduce yp.OutputData.

To uderstand how the past data is mapped to the initial states of the model, see Understand Use of Historical Data for Model Prediction.

Input Arguments

collapse all

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.

Example: predictOptions('InitialCondition','z') specifies zero initial conditions for the measured input-output data.

collapse all

Handling of initial conditions, specified as the comma-separated pair consisting of 'InitialCondition' and one of the following values:

  • '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.

  • 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 in the prediction:

    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 an example, see Use Historical Data to Specify Initial Conditions for Model Prediction.

    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.

Input signal offset for time-domain data, specified as the comma-separated pair consisting of 'InputOffset' and one of the following values:

  • [] — No input offsets.

  • A column vector of length Nu, where Nu is the number of inputs. The software subtracts the offset value InputOffset(i) from the ith input signal before using the input to predict the model response.

  • Nu-by-Ne matrix — For multi-experiment data, specify InputOffset as an Nu-by-Ne matrix, where Ne is the number of experiments. The software subtracts the offset value InputOffset(i,j) from the ith input signal of the jth experiment before prediction.

    If you specify a column vector of length Nu, then the offset value InputOffset(i) is subtracted from the ith input signal of all the experiments.

Output signal offset for time-domain data, specified as the comma-separated pair consisting of 'OutputOffset' and one of the following values:

  • [] — No output offsets.

  • A column vector of length Ny, where Ny is the number of outputs. The software subtracts the offset value OutputOffset(i) from the ith output signal before using the output to predict the model response. After prediction, the software adds the offsets to the predicted response to give the final predicted response.

  • Ny-by-Ne matrix — For multi-experiment data, specify OutputOffset as an Ny-by-Ne matrix, where Ne is the number of experiments. The software subtracts the offset value OutputOffset(i,j) from the ith output signal of the jth experiment before prediction.

    If you specify a column vector of length Ny, then the offset value OutputOffset(i) is subtracted from the ith output signal of all the experiments.

    After prediction, the software adds the removed offsets to the predicted response to give the final predicted response.

Weight of output for initial condition estimation, specified as the comma-separated pair consisting of 'OutputWeight' and one of the following values:

  • [] — No weighting is used by the software for initial condition estimation. This option is the same as using eye(Ny) for the output weight, where Ny is the number of outputs.

  • 'noise' — The software uses the inverse of the NoiseVariance property of the model as the weight.

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

OutputWeight is relevant only for multi-output models.

Output Arguments

collapse all

Option set for predict, retuned as a predictOptions option set.

Introduced in R2012a

Was this topic helpful?