User`s guide

Identifying Input-Output Polynomial Models

m = arx(data,[na nb nk],'Property1',Value1,...,

'PropertyN',ValueN)

% Using IV method

m = iv4(data,[na nb nk],'Property1',Value1,...,

'PropertyN',ValueN)

data

is the estimation da t a and [na nb nk] speciﬁes the model orders, as

discussed in “What Are Black-Box Polynomial Models?” on page 3-41.

The property-value pairs specify any model properties that conﬁgure the

estimation algorithm and the initial conditions. For more information about

accessing and setting m odel properties, see “Model Properties” on page 2-14.

Note You can specify all property-value pairs as a comma-separated list.

To get discrete-time models, use the time-do m ain data (iddata object). To get

a single-output continuous-time model, apply

d2c to a discrete-tim e model

or use continuous-time f requency-domain d ata—either

idfrd object, or

frequency-domain

iddata with Ts=0.

Note The System Identiﬁcation Toolbox product does not support

multip le-output continu o us-time

idarx models.

For more information about validating you model, s ee “Overview of Model

Validation and Plots” on page 8-2.

You can use

pem to reﬁne parameter estimates of an existing po ly nomial

model, as described in “Reﬁning Linear Parametric Models” on p age 3 -103.

For detailed information abou t these commands, see the co rresponding

reference page.

Using pem to Estimate Polynomial Models

You can estimate any single-output polynomial model using the iterative

prediction-error estimation method

pem. For Gaussian disturbances, this

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