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Functions in System Identification Toolbox

  • By Category | Alphabetical List
  • Data Preparation

    Represent Data

    iddata Time- or frequency-domain data
    idfrd Frequency-response data or model
    idinput Generate input signals
    sim Simulate response of identified models to arbitrary inputs
    size Query output/input/array dimensions of input–output model and number of frequencies of FRD model

    Select Data for Estimation

    fselect Select frequency points or range in FRD model
    getexp Specific experiments from multiple-experiment data set
    merge (iddata) Merge data sets into iddata object
    fcat Concatenate FRD models along frequency dimension

    Analyze Data

    bode Bode plot of frequency response, magnitude and phase of frequency response
    bodemag Bode magnitude response of LTI models
    plot Plot input-output data
    advice Analysis and recommendations for data or estimated linear models
    delayest Estimate time delay (dead time) from data
    isreal Determine whether model parameters or data values are real
    realdata Determine whether iddata is based on real-valued signals
    feedback Identify possible feedback data
    pexcit Level of excitation of input signals
    impulseest Nonparameteric impulse response estimation
    etfe Estimate empirical transfer functions and periodograms
    spa Estimate frequency response with fixed frequency resolution using spectral analysis
    spafdr Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
    iddataplotOptions Options set for iddata/plot

    Preprocess Data

    detrend Subtract offset or trend from data signals
    retrend Add offsets or trends to data signals
    diff Difference signals in iddata objects
    idfilt Filter data using user-defined passbands, general filters, or Butterworth filters
    misdata Reconstruct missing input and output data
    nkshift Shift data sequences
    idresamp Resample time-domain data by decimation or interpolation
    resample Resample time-domain data by decimation or interpolation (requires Signal Processing Toolbox software)
    getTrend Data offset and trend information
    chgFreqUnit Change frequency units of frequency-response data model
    fdel Delete specified data from frequency response data (FRD) models
    TrendInfo Offset and linear trend slope values for detrending data

    Transform Data

    fft Transform iddata object to frequency domain data
    ifft Transform iddata objects from frequency to time domain
    etfe Estimate empirical transfer functions and periodograms
    spa Estimate frequency response with fixed frequency resolution using spectral analysis
    spafdr Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution

    Linear Model Identification

    Process Models

    procest Estimate process model using time or frequency data
    idproc Continuous-time process model with identifiable parameters
    pem Prediction error estimate for linear or nonlinear model
    idpar Create parameter for initial states and input level estimation
    delayest Estimate time delay (dead time) from data
    init Set or randomize initial parameter values
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getpar Obtain attributes such as values and bounds of linear model parameters
    setpar Set attributes such as values and bounds of linear model parameters
    procestOptions Options set for procest
    findstatesOptions Option set for findstates

    Input-Output Polynomial Models

    arx Estimate parameters of ARX or AR model using least squares
    armax Estimate parameters of ARMAX model using time-domain data
    bj Estimate Box-Jenkins polynomial model using time domain data
    iv4 ARX model estimation using four-stage instrumental variable method.
    ivx ARX model estimation using instrumental variable method with arbitrary instruments
    oe Estimate Output-Error polynomial model using time or frequency domain data
    polyest Estimate polynomial model using time- or frequency-domain data
    pem Prediction error estimate for linear or nonlinear model
    idpoly Polynomial model with identifiable parameters
    arxstruc Compute and compare loss functions for single-output ARX models
    ivstruc Loss functions for sets of ARX model structures
    selstruc Select model order for single-output ARX models
    struc Generate model-order combinations for single-output ARX model estimation
    arxRegul Determine regularization constants for ARX model estimation
    delayest Estimate time delay (dead time) from data
    init Set or randomize initial parameter values
    polydata Access polynomial coefficients and uncertainties of identified model
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getpar Obtain attributes such as values and bounds of linear model parameters
    setpar Set attributes such as values and bounds of linear model parameters
    setPolyFormat Specify format for B and F polynomials of multi-input polynomial model for backward compatibility
    rarmax Estimate recursively parameters of ARMAX or ARMA models
    rarx Estimate parameters of ARX or AR models recursively
    rbj Estimate recursively parameters of Box-Jenkins models
    roe Estimate recursively output-error models (IIR-filters)
    rpem Estimate general input-output models using recursive prediction-error minimization method
    rplr Estimate general input-output models using recursive pseudolinear regression method
    segment Segment data and estimate models for each segment
    armaxOptions Option set for armax
    arxOptions Option set for ar
    arxRegulOptions Option set for arxRegul
    bjOptions Option set for bj
    iv4Options Option set for iv4
    oeOptions Option set for oe
    polyestOptions Option set for polyest

    State-Space Models

    ssest Estimate state-space model using time or frequency domain data
    ssregest Estimate state-space model by reduction of regularized ARX model
    n4sid Estimate state-space model using a subspace method.
    idss State-space model with identifiable parameters
    pem Prediction error estimate for linear or nonlinear model
    delayest Estimate time delay (dead time) from data
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getpar Obtain attributes such as values and bounds of linear model parameters
    setpar Set attributes such as values and bounds of linear model parameters
    ssform Quick configuration of state-space model structure
    init Set or randomize initial parameter values
    idpar Create parameter for initial states and input level estimation
    idssdata State-space data of identified system
    findstates(idParametric) Estimate initial states of identified linear state-space model from data
    ssestOptions Option set for ssest
    ssregestOptions Options set for ssregest
    n4sidOptions Option set for n4sid

    Transfer Function Models

    tfest Transfer function estimation
    idtf Transfer function model with identifiable parameters
    pem Prediction error estimate for linear or nonlinear model
    delayest Estimate time delay (dead time) from data
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getpar Obtain attributes such as values and bounds of linear model parameters
    setpar Set attributes such as values and bounds of linear model parameters
    tfdata Access transfer function data
    init Set or randomize initial parameter values
    tfestOptions Options set for tfest

    Linear Grey-Box Models

    greyest Linear grey-box model estimation
    idgrey Linear ODE (grey-box model) with identifiable parameters
    findstates(idParametric) Estimate initial states of identified linear state-space model from data
    init Set or randomize initial parameter values
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getpar Obtain attributes such as values and bounds of linear model parameters
    setpar Set attributes such as values and bounds of linear model parameters

    Frequency-Response Models

    etfe Estimate empirical transfer functions and periodograms
    spa Estimate frequency response with fixed frequency resolution using spectral analysis
    spafdr Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
    idfrd Frequency-response data or model
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    bode Bode plot of frequency response, magnitude and phase of frequency response
    bodemag Bode magnitude response of LTI models
    freqresp Frequency response over grid
    chgFreqUnit Change frequency units of frequency-response data model

    Correlation Models

    cra Estimate impulse response using prewhitened-based correlation analysis
    impulseest Nonparameteric impulse response estimation
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    impulseestOptions Options set for impulseest

    Nonlinear Model Identification

    Nonlinear ARX Models

    nlarx Estimate nonlinear ARX model
    idnlarx Nonlinear ARX model
    pem Prediction error estimate for linear or nonlinear model
    customnet Custom nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
    linear Class representing linear nonlinearity estimator for nonlinear ARX models
    neuralnet Class representing neural network nonlinearity estimator for nonlinear ARX models
    polyreg Powers and products of standard regressors
    treepartition Class representing binary-tree nonlinearity estimator for nonlinear ARX models
    wavenet Class representing wavelet network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
    customreg Custom regressor for nonlinear ARX models
    sigmoidnet Class representing sigmoid network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
    addreg Add custom regressors to nonlinear ARX model
    getreg Regressor expressions and numerical values in nonlinear ARX model
    evaluate Value of nonlinearity estimator at given input
    plot Plot nonlinearity of nonlinear ARX model
    sim(idnlarx) Simulate nonlinear ARX model
    findop(idnlarx) Compute operating point for nonlinear ARX model
    operspec(idnlarx) Construct operating point specification object for idnlarx model
    linearize(idnlarx) Linearize nonlinear ARX model
    linapp Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input
    findstates(idnlarx) Estimate initial states of nonlinear ARX model from data
    data2state(idnlarx) Map past input/output data to current states of nonlinear ARX model
    init Set or randomize initial parameter values
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getDelayInfo Get input/output delay information for idnlarx model structure

    Hammerstein-Wiener Models

    nlhw Estimate Hammerstein-Wiener model
    idnlhw Hammerstein-Wiener model
    pem Prediction error estimate for linear or nonlinear model
    customnet Custom nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
    deadzone Class representing dead-zone nonlinearity estimator for Hammerstein-Wiener models
    poly1d Class representing single-variable polynomial nonlinear estimator for Hammerstein-Wiener models
    pwlinear Class representing piecewise-linear nonlinear estimator for Hammerstein-Wiener models
    saturation Class representing saturation nonlinearity estimator for Hammerstein-Wiener models
    sigmoidnet Class representing sigmoid network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
    unitgain Specify absence of nonlinearities for specific input or output channels in Hammerstein-Wiener models
    wavenet Class representing wavelet network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
    evaluate Value of nonlinearity estimator at given input
    plot Plot input and output nonlinearity, and linear responses of Hammerstein-Wiener model
    sim(idnlhw) Simulate Hammerstein-Wiener model
    findop(idnlhw) Compute operating point for Hammerstein-Wiener model
    operspec(idnlhw) Construct operating point specification object for idnlhw model
    linearize(idnlhw) Linearize Hammerstein-Wiener model
    linapp Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input
    findstates(idnlhw) Estimate initial states of nonlinear Hammerstein-Wiener model from data
    init Set or randomize initial parameter values
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters

    Nonlinear Grey-Box Models

    pem Prediction error estimate for linear or nonlinear model
    idnlgrey Nonlinear ODE (grey-box model) with unknown parameters
    findstates(idnlgrey) Estimate initial states of nonlinear grey-box model from data
    init Set or randomize initial parameter values
    getinit Values of idnlgrey model initial states
    setinit Set initial states of idnlgrey model object
    getpar Parameter values and properties of idnlgrey model parameters
    setpar Set initial parameter values of idnlgrey model object
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    sim(idnlgrey) Simulate nonlinear ODE model

    Grey-Box Model Estimation

    greyest Linear grey-box model estimation
    pem Prediction error estimate for linear or nonlinear model
    idgrey Linear ODE (grey-box model) with identifiable parameters
    idnlgrey Nonlinear ODE (grey-box model) with unknown parameters
    findstates(idParametric) Estimate initial states of identified linear state-space model from data
    findstates(idnlgrey) Estimate initial states of nonlinear grey-box model from data
    init Set or randomize initial parameter values
    getinit Values of idnlgrey model initial states
    setinit Set initial states of idnlgrey model object
    getpar Parameter values and properties of idnlgrey model parameters
    setpar Set initial parameter values of idnlgrey model object
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    sim(idnlgrey) Simulate nonlinear ODE model
    greyestOptions Option set for greyest

    Time-Series Model Identification

    ar Estimate parameters of AR model for scalar time series
    armax Estimate parameters of ARMAX model using time-domain data
    arx Estimate parameters of ARX or AR model using least squares
    etfe Estimate empirical transfer functions and periodograms
    spa Estimate frequency response with fixed frequency resolution using spectral analysis
    spafdr Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
    ivar AR model estimation using instrumental variable method
    n4sid Estimate state-space model using a subspace method.
    ssest Estimate state-space model using time or frequency domain data
    pem Prediction error estimate for linear or nonlinear model
    nlarx Estimate nonlinear ARX model
    idpoly Polynomial model with identifiable parameters
    idss State-space model with identifiable parameters
    idnlarx Nonlinear ARX model
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    init Set or randomize initial parameter values
    noise2meas Noise component of model
    spectrum Output power spectrum of time series models
    forecast Forecast linear system response into future
    arOptions Option set for ar
    forecastOptions Option set for forecast
    simOptions Option set for sim

    Model Validation

    Compare Output with Measured Data

    compare Compare model output and measured output
    goodnessOfFit Goodness of fit between test and reference data
    findstates(idParametric) Estimate initial states of identified linear state-space model from data
    idpar Create parameter for initial states and input level estimation
    compareOptions Option set for compare

    Residual Analysis

    resid Compute and test model residuals (prediction errors)
    pe Prediction error for an identified model
    fpe Akaike Final Prediction Error for estimated model
    aic Akaike Information Criterion for estimated model
    peOptions Option set for pe

    Uncertainty Analysis

    present Display model information, including estimated uncertainty
    simsd Simulate linear models with uncertainty using Monte Carlo method
    freqresp Frequency response over grid
    rsample Random sampling of linear identified systems
    showConfidence Display confidence regions on response plots for identified models
    getcov Parameter covariance of linear identified parametric model
    setcov Set parameter covariance data in identified model
    translatecov Translate parameter covariance across model operations
    step Step response plot of dynamic system
    stepplot Plot step response and return plot handle
    impulse Impulse response plot of dynamic system; impulse response data
    bode Bode plot of frequency response, magnitude and phase of frequency response
    bodemag Bode magnitude response of LTI models
    nyquist Nyquist plot of frequency response
    nyquistplot Nyquist plot with additional plot customization options
    iopzmap Plot pole-zero map for I/O pairs of model
    iopzplot Plot pole-zero map for I/O pairs and return plot handle
    tfdata Access transfer function data
    zpkdata Access zero-pole-gain data
    simsdOptions Option set for simsd

    Model Analysis

    Continuous- and Discrete-Time Conversions

    c2d Convert model from continuous to discrete time
    d2c Convert model from discrete to continuous time
    d2d Resample discrete-time model
    translatecov Translate parameter covariance across model operations
    c2dOptions Create option set for continuous- to discrete-time conversions
    d2cOptions Create option set for discrete- to continuous-time conversions
    d2dOptions Create option set for discrete-time resampling

    Model Type and Other Transformations

    idfrd Frequency-response data or model
    idpoly Polynomial model with identifiable parameters
    idtf Transfer function model with identifiable parameters
    idss State-space model with identifiable parameters
    canon State-space canonical realization
    noisecnv Transform identified linear model with noise channels to model with measured channels only
    translatecov Translate parameter covariance across model operations
    merge Merge estimated models
    noise2meas Noise component of model
    absorbDelay Replace time delays by poles at z = 0 or phase shift
    chgTimeUnit Change time units of dynamic system
    chgFreqUnit Change frequency units of frequency-response data model
    fdel Delete specified data from frequency response data (FRD) models
    stack Build model array by stacking models or model arrays along array dimensions
    ss2ss State coordinate transformation for state-space model

    Linearization of Nonlinear Models

    linapp Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input
    findop(idnlarx) Compute operating point for nonlinear ARX model
    linearize(idnlarx) Linearize nonlinear ARX model
    findop(idnlhw) Compute operating point for Hammerstein-Wiener model
    linearize(idnlhw) Linearize Hammerstein-Wiener model

    Data Extraction

    polydata Access polynomial coefficients and uncertainties of identified model
    ssdata Access state-space model data
    idssdata State-space data of identified system
    tfdata Access transfer function data
    zpkdata Access zero-pole-gain data
    frdata Access data for frequency response data (FRD) object
    freqresp Frequency response over grid
    getpvec Model parameters and associated uncertainty data
    setpvec Modify value of model parameters
    getcov Parameter covariance of linear identified parametric model
    setcov Set parameter covariance data in identified model
    get Access model property values
    set Set or modify model properties
    nparams Number of model parameters
    ndims Query number of dimensions of dynamic system model or model array
    order Query model order
    pole Compute poles of dynamic system
    zero Zeros and gain of SISO dynamic system
    size Query output/input/array dimensions of input–output model and number of frequencies of FRD model
    damp Natural frequency and damping ratio
    dcgain Low-frequency (DC) gain of LTI system
    bandwidth Frequency response bandwidth

    Simulation and Prediction

    sim Simulate response of identified models to arbitrary inputs
    sim(idnlarx) Simulate nonlinear ARX model
    sim(idnlgrey) Simulate nonlinear ODE model
    sim(idnlhw) Simulate Hammerstein-Wiener model
    simsd Simulate linear models with uncertainty using Monte Carlo method
    predict K-step ahead prediction
    rsample Random sampling of linear identified systems
    forecast Forecast linear system response into future
    idinput Generate input signals
    simOptions Option set for sim
    simsdOptions Option set for simsd
    forecastOptions Option set for forecast
    predictOptions Option set for predict
    forecastOptions Option set for forecast

    Response Computation and Visualization

    sim Simulate response of identified models to arbitrary inputs
    sim(idnlarx) Simulate nonlinear ARX model
    sim(idnlgrey) Simulate nonlinear ODE model
    sim(idnlhw) Simulate Hammerstein-Wiener model
    bode Bode plot of frequency response, magnitude and phase of frequency response
    bodeplot Plot Bode frequency response with additional plot customization options
    bodemag Bode magnitude response of LTI models
    step Step response plot of dynamic system
    stepplot Plot step response and return plot handle
    stepinfo Rise time, settling time, and other step response characteristics
    nyquist Nyquist plot of frequency response
    nyquistplot Nyquist plot with additional plot customization options
    impulse Impulse response plot of dynamic system; impulse response data
    impulseplot Plot impulse response and return plot handle
    pzmap Pole-zero plot of dynamic system
    pzplot Pole-zero map of dynamic system model with plot customization options
    iopzmap Plot pole-zero map for I/O pairs of model
    iopzplot Plot pole-zero map for I/O pairs and return plot handle
    spectrum Output power spectrum of time series models
    spectrumplot Plot disturbance spectrum of linear identified models
    ffplot Compute and plot frequency response magnitude and phase for linear frequencies
    showConfidence Display confidence regions on response plots for identified models
    lsim Simulate time response of dynamic system to arbitrary inputs
    lsimplot Simulate response of dynamic system to arbitrary inputs and return plot handle
    lsiminfo Compute linear response characteristics
    identpref Set System Identification Toolbox preferences
    findstates(idnlarx) Estimate initial states of nonlinear ARX model from data
    findstates(idnlgrey) Estimate initial states of nonlinear grey-box model from data
    findstates(idnlhw) Estimate initial states of nonlinear Hammerstein-Wiener model from data
    stepDataOptions Options set for step
    bodeoptions Create list of Bode plot options
    nyquistoptions List of Nyquist plot options
    timeoptions Create list of time plot options
    getoptions Return @PlotOptions handle or plot options property
    setoptions Set plot options for response plot
    pzoptions Create list of pole/zero plot options
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