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System Identification Toolbox Functions - By Category

Alphabetical List By Category

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 model
`size`	Query output/input/array dimensions of input–output model and number of frequencies of FRD model
`midprefs`	Specify location for file containing System Identification app startup information
`simOptions`	Option set for sim

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, or magnitude and phase data
`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`	Nonparametric 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`	Option set for iddata/plot

Preprocess Data

`detrend`	Subtract offset or trend from time-domain signals contained in iddata objects
`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 and 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

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
`idpoly`	Polynomial model with identifiable parameters
`pem`	Prediction error estimate for linear and nonlinear model
`arxstruc`	Compute loss functions for single-output ARX models
`ivstruc`	Compute loss functions for sets of ARX model structures using instrumental variable method
`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
`armaxOptions`	Option set for armax
`arxOptions`	Option set for arx
`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 subspace method
`idss`	State-space model with identifiable parameters
`pem`	Prediction error estimate for linear and 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`	Estimate initial states of model
`ssestOptions`	Option set for ssest
`ssregestOptions`	Option set for ssregest
`n4sidOptions`	Option set for n4sid
`findstatesOptions`	Option set for findstates

Transfer Function Models

`tfest`	Transfer function estimation
`idtf`	Transfer function model with identifiable parameters
`pem`	Prediction error estimate for linear and 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`	Option set for tfest

Linear Grey-Box Models

`greyest`	Linear grey-box model estimation
`idgrey`	Linear ODE (grey-box model) with identifiable parameters
`pem`	Prediction error estimate for linear and nonlinear model
`findstates`	Estimate initial states of model
`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
`findstatesOptions`	Option set for findstates
`greyestOptions`	Option set for greyest

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, or magnitude and phase data
`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`	Nonparametric 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

`idnlarx`	Nonlinear ARX model
`nlarx`	Estimate parameters of nonlinear ARX model
`nlarxOptions`	Option set for nlarx
`isnlarx`	Detect nonlinearity in estimation data
`init`	Set or randomize initial parameter values
`getpvec`	Model parameters and associated uncertainty data
`setpvec`	Modify value of model parameters
`polyreg`	Powers and products of standard regressors
`customreg`	Custom regressor for nonlinear ARX models
`addreg`	Add custom regressors to nonlinear ARX model
`getreg`	Regressor expressions and numerical values in nonlinear ARX 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
`treepartition`	Class representing binary-tree nonlinearity estimator for nonlinear ARX models
`wavenet`	Create a wavelet network nonlinearity estimator object
`sigmoidnet`	Class representing sigmoid network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
`evaluate`	Value of nonlinearity estimator at given input
`sim`	Simulate response of identified model
`simOptions`	Option set for sim
`predict`	Predict K-step ahead model output
`predictOptions`	Option set for predict
`compare`	Compare identified model output and measured output
`compareOptions`	Option set for compare
`forecast`	Forecast identified model output
`forecastOptions`	Option set for forecast
`plot`	Plot nonlinearity of nonlinear ARX model
`evaluate`	Value of nonlinearity estimator at given input
`getDelayInfo`	Get input/output delay information for idnlarx model structure
`findop`	Compute operating point for Nonlinear ARX model
`findopOptions`	Option set for findop
`operspec`	Construct operating point specification object for idnlarx model
`linearize`	Linearize nonlinear ARX model
`linapp`	Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input

Hammerstein-Wiener Models

`idnlhw`	Hammerstein-Wiener model
`nlhw`	Estimate Hammerstein-Wiener model
`nlhwOptions`	Option set for nlhw
`init`	Set or randomize initial parameter values
`getpvec`	Model parameters and associated uncertainty data
`setpvec`	Modify value of model parameters
`customnet`	Custom nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
`deadzone`	Create a dead-zone nonlinearity estimator object
`poly1d`	Class representing single-variable polynomial nonlinear estimator for Hammerstein-Wiener models
`pwlinear`	Create a piecewise-linear nonlinearity estimator object
`saturation`	Create a saturation nonlinearity estimator object
`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`	Create a wavelet network nonlinearity estimator object
`evaluate`	Value of nonlinearity estimator at given input
`sim`	Simulate response of identified model
`simOptions`	Option set for sim
`compare`	Compare identified model output and measured output
`compareOptions`	Option set for compare
`plot`	Plot input and output nonlinearity, and linear responses of Hammerstein-Wiener model
`evaluate`	Value of nonlinearity estimator at given input
`findop`	Compute operating point for Hammerstein-Wiener model
`findopOptions`	Option set for findop
`operspec`	Construct operating point specification object for idnlhw model
`linearize`	Linearize Hammerstein-Wiener model
`linapp`	Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input

Nonlinear Grey-Box Models

`nlgreyest`	Estimate nonlinear grey-box model parameters
`idnlgrey`	Nonlinear grey-box model
`pem`	Prediction error estimate for linear and nonlinear model
`findstates`	Estimate initial states of model
`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`	Simulate response of identified model
`nlgreyestOptions`	Option set for nlgreyest
`findstatesOptions`	Option set for findstates
`simOptions`	Option set for sim

Grey-Box Model Estimation

`greyest`	Linear grey-box model estimation
`nlgreyest`	Estimate nonlinear grey-box model parameters
`idgrey`	Linear ODE (grey-box model) with identifiable parameters
`idnlgrey`	Nonlinear grey-box model
`pem`	Prediction error estimate for linear and nonlinear model
`findstates`	Estimate initial states of model
`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`	Simulate response of identified model
`greyestOptions`	Option set for greyest
`nlgreyestOptions`	Option set for nlgreyest
`findstatesOptions`	Option set for findstates
`simOptions`	Option set for sim

Model Validation

Compare Output with Measured Data

`compare`	Compare identified model output and measured output
`compareOptions`	Option set for compare
`goodnessOfFit`	Goodness of fit between test and reference data
`predict`	Predict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter
`predictOptions`	Option set for predict
`sim`	Simulate response of identified model
`simOptions`	Option set for sim
`findstates`	Estimate initial states of model
`findstatesOptions`	Option set for findstates
`data2state`	Map past data to states of state-space and nonlinear ARX models
`idpar`	Create parameter for initial states and input level estimation

Residual Analysis

`resid`	Compute and test residuals
`pe`	Prediction error for identified model
`fpe`	Akaike’s Final Prediction Error for estimated model
`aic`	Akaike’s Information Criterion for estimated model
`residOptions`	Option set for resid
`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 identified model
`setcov`	Set parameter covariance data in identified model
`translatecov`	Translate parameter covariance across model transformation operations
`step`	Step response plot of dynamic system; step response data
`stepplot`	Plot step response and return plot handle
`impulse`	Impulse response plot of dynamic system; impulse response data
`bode`	Bode plot of frequency response, or magnitude and phase data
`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 transformation 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
`balred`	Model order reduction
`noisecnv`	Transform identified linear model with noise channels to model with measured channels only
`translatecov`	Translate parameter covariance across model transformation operations
`merge`	Merge estimated models
`append`	Group models by appending their inputs and outputs
`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
`idnlarx/findop`	Compute operating point for Nonlinear ARX model
`idnlarx/linearize`	Linearize nonlinear ARX model
`idnlhw/findop`	Compute operating point for Hammerstein-Wiener model
`idnlhw/linearize`	Linearize Hammerstein-Wiener model
`findopOptions`	Option set for findop

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 identified 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`	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 model
`simOptions`	Option set for sim
`simsd`	Simulate linear models with uncertainty using Monte Carlo method
`simsdOptions`	Option set for simsd
`predict`	Predict K-step ahead model output
`predictOptions`	Option set for predict
`forecast`	Forecast identified model output
`forecastOptions`	Option set for forecast
`idinput`	Generate input signals

Response Computation and Visualization

`sim`	Simulate response of identified model
`bode`	Bode plot of frequency response, or magnitude and phase data
`bodeplot`	Plot Bode frequency response with additional plot customization options
`bodemag`	Bode magnitude response of LTI models
`step`	Step response plot of dynamic system; step response data
`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 plot 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
`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
`showConfidence`	Display confidence regions on response plots for identified models
`findstates`	Estimate initial states of model
`data2state`	Map past data to states of state-space and nonlinear ARX models
`simOptions`	Option set for sim
`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
`spectrumoptions`	Option set for spectrumplot
`identpref`	Set System Identification Toolbox preferences
`findstatesOptions`	Option set for findstates

Time Series Analysis

`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 subspace method
`ssest`	Estimate state-space model using time or frequency domain data
`pem`	Prediction error estimate for linear and nonlinear model
`nlarx`	Estimate parameters of 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 identified model output
`sim`	Simulate response of identified model
`arOptions`	Option set for ar
`forecastOptions`	Option set for forecast
`simOptions`	Option set for sim

Online Estimation

Online Parameter Estimation

`recursiveAR`	Create System object for online parameter estimation of AR model
`recursiveARMA`	Create System object for online parameter estimation of ARMA model
`recursiveARX`	Create System object for online parameter estimation of ARX model
`recursiveARMAX`	Create System object for online parameter estimation of ARMAX model
`recursiveBJ`	Create System object for online parameter estimation of Box-Jenkins polynomial model
`recursiveOE`	Create System object for online parameter estimation of Output-Error polynomial model
`recursiveLS`	Create System object for online parameter estimation using recursive least squares algorithm
`step`	Update model parameters and output online using recursive estimation algorithm
`clone`	Copy online parameter estimation System object
`reset`	Reset online parameter estimation System object
`release`	Unlock online parameter estimation System object
`isLocked`	Locked status of online parameter estimation System object
`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

Online State Estimation

`extendedKalmanFilter`	Create extended Kalman filter object for online state estimation
`unscentedKalmanFilter`	Create unscented Kalman filter object for online state estimation
`particleFilter`	Particle filter object for online state estimation
`correct`	Correct state and state estimation error covariance using extended or unscented Kalman filter, or particle filter and measurements
`predict`	Predict state and state estimation error covariance at next time step using extended or unscented Kalman filter, or particle filter
`initialize`	Initialize the state of the particle filter
`clone`	Copy online state estimation object

System Identification Toolbox Documentation

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