Documentation

iv4Options

Option set for iv4

Syntax

opt = iv4Options
opt = iv4Options(Name,Value)

Description

opt = iv4Options creates the default options set for iv4.

opt = iv4Options(Name,Value) creates an option set with the options specified by one or more Name,Value pair arguments.

Input Arguments

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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' — Handling of initial conditions'auto' (default) | 'zero' | 'estimate' | 'backcast'

Handling of initial conditions during estimation, specified as one of the following strings:

  • 'zero' — The initial condition is set to zero.

  • 'estimate' — The initial condition is treated as an independent estimation parameter.

  • 'backcast' — The initial condition is estimated using the best least squares fit.

  • 'auto' — The software chooses the initial condition handling method based on the estimation data.

'Focus' — Estimation focus'prediction' (default) | 'simulation' | 'stability' | vector | matrix | linear system

Estimation focus that defines how the errors e between the measured and the modeled outputs are weighed at specific frequencies during the minimization of the prediction error, specified as one of the following values:

  • 'prediction' — Automatically calculates the weighting function as a product of the input spectrum and the inverse of the noise spectrum. The weighting function minimizes the one-step-ahead prediction. This approach typically favors fitting small time intervals (higher frequency range). From a statistical-variance point of view, this weighting function is optimal. However, this method neglects the approximation aspects (bias) of the fit.

    This option focuses on producing a good predictor and does not enforce model stability. Use 'stability' when you want to ensure a stable model.

  • 'simulation' — Estimates the model using the frequency weighting of the transfer function that is given by the input spectrum. Typically, this method favors the frequency range where the input spectrum has the most power. This method provides a stable model.

  • 'stability' — Same as 'prediction', but with model stability enforced.

  • Passbands — Row vector or matrix containing frequency values that define desired passbands. For example:

    [wl,wh]
    [w1l,w1h;w2l,w2h;w3l,w3h;...]

    where wl and wh represent lower and upper limits of a passband. For a matrix with several rows defining frequency passbands, the algorithm uses union of frequency ranges to define the estimation passband.

    Passbands are expressed in rad/TimeUnit for time-domain data and in FrequencyUnit for frequency-domain data, where TimeUnit and FrequencyUnit are the time and frequency units of the estimation data.

  • SISO filter — Specify a SISO linear filter in one of the following ways:

    • A single-input-single-output (SISO) linear system

    • {A,B,C,D} format, which specifies the state-space matrices of the filter

    • {numerator, denominator} format, which specifies the numerator and denominator of the filter transfer function

      This option calculates the weighting function as a product of the filter and the input spectrum to estimate the transfer function. To obtain a good model fit for a specific frequency range, you must choose the filter with a passband in this range. The estimation result is the same if you first prefilter the data using idfilt.

  • Weighting vector — For frequency-domain data only, specify a column vector of weights. This vector must have the same length as the frequency vector of the data set, Data.Frequency. Each input and output response in the data is multiplied by the corresponding weight at that frequency.

'EstCovar' — Control whether to generate parameter covariance datatrue (default) | false

Controls whether parameter covariance data is generated, specified as true or false.

If EstCovar is true, then use getcov to fetch the covariance matrix from the estimated model.

'Display' — Specify whether to display the estimation progress'off' (default) | 'on'

Specify whether to display the estimation progress, specified as one of the following strings:

  • 'on' — Information on model structure and estimation results are displayed in a progress-viewer window.

  • 'off' — No progress or results information is displayed.

'InputOffset' — Removal of offset from time-domain input data during estimation[] (default) | vector of positive integers | matrix

Removal of offset from time-domain input data during estimation, specified as the comma-separated pair consisting of 'InputOffset' and one of the following:

  • A column vector of positive integers of length Nu, where Nu is the number of inputs.

  • [] — Indicates no offset.

  • Nu-by-Ne matrix — 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.

'OutputOffset' — Removal of offset from time-domain output data during estimation[] (default) | vector | matrix

Removal of offset from time-domain output data during estimation, specified as the comma-separated pair consisting of 'OutputOffset' and one of the following:

  • A column vector of length Ny, where Ny is the number of outputs.

  • [] — Indicates no offset.

  • Ny-by-Ne matrix — For 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.

'Advanced' — Additional advanced optionsstructure

Additional advanced options, specified as a structure with the following fields:

  • MaxSize — Specifies the maximum number of elements in a segment when input-output data is split into segments.

    MaxSize must be a positive integer.

    Default: 250000

  • StabilityThreshold — Specifies thresholds for stability tests.

    StabilityThreshold is a structure with the following fields:

    • s — Specifies the location of the right-most pole to test the stability of continuous-time models. A model is considered stable when its right-most pole is to the left of s.

      Default: 0

    • z — Specifies the maximum distance of all poles from the origin to test stability of discrete-time models. A model is considered stable if all poles are within the distance z from the origin.

      Default: 1+sqrt(eps)

Output Arguments

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opt — Options set for iv4iv4Options option set

Option set for iv4, returned as an iv4Options option set.

Examples

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Specify Options for ARX Model Estimation Using 4-Stage Instrument Variable Method

Create an options set for iv4 using the 'backcast' algorithm to initialize the state. Set Display to 'on'.

opt = iv4Options('InitialCondition','backcast','Display','on');

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

opt = iv4Options;
opt.InitialCondition = 'backcast';
opt.Display = 'on';

See Also

Introduced in R2012a

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