System Identification Toolbox

Command Reference

This chapter contains detailed descriptions of all of the functions in the System Identification Toolbox. It begins with a list of functions grouped by subject area and continues with the entries in alphabetical order. Information is also available through the online Help facility.

By typing a function name without arguments, you also get immediate syntax help about its arguments.

For ease of use, most functions have several default arguments. The Synopsis first lists the function with the necessary input arguments and then with all the possible input arguments. The functions can be used with any number of arguments between these extremes. The rule is that missing, trailing arguments are given default values, as defined in the manual. Default values are also obtained by entering the arguments as the empty matrix [ ].

MATLAB does not require that you specify all of the output arguments; those not specified are not returned. For functions with several output arguments in the System Identification Toolbox, missing arguments are, as a rule, not computed, in order to save time.


	The Graphical User Interface
`ident`	Open the interface.
`midprefs`	Set directory where to store start-up information.


	Simulation and Prediction
`idinput`	Generate input signals.
`idsim`	Simulate a general linear system.
`pe`	Compute prediction errors.
`poly2th`	Create a model structure for input-output models defined as numerator and denominator polynomials.
`predict`	Compute predictions according to model.


	Data Manipulation
`dtrend`	Remove trends from data.
`idfilt`	Filter data.
`idresamp`	Resample data.


	Nonparametric Estimation
`covf`	Estimate covariance function.
`cra`	Estimate impulse response and covariance functions using correlation analysis.
`etfe`	Estimate spectra and transfer functions using direct Fourier techniques.
`spa`	Estimate spectra and transfer functions using spectral analysis.


	Parameter Estimation
`ar`	Estimate AR model.
`armax`	Estimate ARMAX model.
`arx`	Estimate ARX model using least squares.
`bj`	Estimate Box-Jenkins model.
`canstart`	Estimate multivariate models in canonical state-space form.
`ivar`	Estimate AR model using instrumental variable methods.
`ivx`	Estimate ARX model using general instruments.
`iv4`	Estimate ARX model using four-stage instrumental variable method.
`oe`	Estimate Output-Error model.
`n4sid`	Estimate state-space model using subspace method.
`pem`	Estimate general linear model.


	Model Structure Creation
`arx2th`	Define (multivariate) ARX structures.
`canform`	Generate canonical forms.
`mf2th`	Create arbitrary linear model structure via an M-file that you write.
`modstruc`	Define state-space models with known and unknown parameters.
`ms2th`	Create model structure for linear state-space models with known and unknown parameters.
`poly2th`	Create a model structure for input-output models defined as numerator and denominator polynomials.


	Manipulating Model Structures
`fixpar`	Fix parameters in structures to given values.
`sett`	Set the sampling interval.
`ss2th`	Transform a state-space model to a parametrized canonical form.
`thinit`	Select or randomize initial parameter values.
`unfixpar`	Allow certain earlier fixed parameters be estimated.


	Model Conversions
`idmodred`	Reduce a model to lower order.
`thc2thd`	Transform from continuous to discrete time.
`thd2thc`	Transform from discrete to continuous time.
`th2arx`	Theta to ARX parameters.
`th2ff`	Theta to frequency functions and spectra.
`th2par`	Theta to estimated parameters and variances.
`th2poly`	Theta to transfer function polynomials.
`th2ss`	Theta to state-space matrices.
`th2tf`	Theta to transfer functions.
`th2zp`	Theta to zeros, poles, and static gains.


	Model Presentation
`bodeplot`	Plot Bode diagrams.
`ffplot`	Plot frequency functions and spectra.
`idplot`	Display input-output data.
`nyqplot`	Plot Nyquist diagrams.
`present`	Display model on screen.
`zpplot`	Plot zeros and poles.







Information Extraction



getff

Extract the frequency functions from the freqfunc format.


gett

Extract the sampling interval from the theta format.


getmfth

Extract the M-file name that defines the model structure.


getncap

Extract from the theta format the number of data upon which model is based.


getzp

Extract the zeros and poles from the zepo format.


th2par

Extract estimated parameters and variances from the theta format.

	Information Extraction
`getff`	Extract the frequency functions from the freqfunc format.
`gett`	Extract the sampling interval from the theta format.
`getmfth`	Extract the M-file name that defines the model structure.
`getncap`	Extract from the theta format the number of data upon which model is based.
`getzp`	Extract the zeros and poles from the zepo format.
`th2par`	Extract estimated parameters and variances from the theta format.


	Model Validation
`compare`	Compare model's simulated or predicted output with actual output.
`idsim`	Simulate a model.
`pe`	Compute prediction errors.
`predict`	Predict future outputs.
`resid`	Compute and test model residuals.


	Assessing Model Uncertainty
`idsimsd`	Simulate responses from several possible models.
`th2ff`	Compute frequency function and its standard deviation.
`th2zp`	Compute zeros, poles, static gains, and their standard deviations.

	Model Structure Selection
`arxstruc`	Compute loss functions for sets of ARX model structure.
`ivstruc`	Compute loss functions for sets of output error model structures.
`selstruc`	Select structure.
`struc`	Generate sets of structures.

Model Structure Selection

arxstruc
Compute loss functions for sets of ARX model structure.

ivstruc
Compute loss functions for sets of output error model structures.

selstruc
Select structure.

struc
Generate sets of structures.







Recursive Parameter Estimation



rarmax

Estimate ARMAX or ARMA models recursively.


rarx

Estimate ARX or AR models recursively. 


rbj

Estimate Box-Jenkins models recursively.


roe

Estimate Output-Error models (IIR-filters) recursively.


rpem

Estimate general input-output models using a recursive prediction error method.


rplr

Estimate general input-output models using a recursive pseudo-linear regression method.


segment

Segment data and estimate models for each segment.

	Recursive Parameter Estimation
`rarmax`	Estimate ARMAX or ARMA models recursively.
`rarx`	Estimate ARX or AR models recursively.
`rbj`	Estimate Box-Jenkins models recursively.
`roe`	Estimate Output-Error models (IIR-filters) recursively.
`rpem`	Estimate general input-output models using a recursive prediction error method.
`rplr`	Estimate general input-output models using a recursive pseudo-linear regression method.
`segment`	Segment data and estimate models for each segment.

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