User`s guide

Identifying Low-Order Transfer Functions (Process M odels)

You can export the model to the MATLAB w orkspace for further analysis

by dragging it to the To Workspace rectangle in the System Identiﬁcation

Tool GUI.

Estimating Process Models at the Command Line

• “Using pem to E stimate Pro cess Models” on page 3-29

• “Example – Estimating Process Models with Free Parameters at the

Command Line” on page 3-30

• “Exam p le – Estimating Proce ss M od el s with Fi xe d P a ra m eters a t the

Command Line” on page 3-32

Using pem to Estimate Process Models

You can estimate process models using the iterative estimation method pem

that minimizes the prediction errors to obtain maximum likelihood estimates.

The resulting models are stored as

idproc model objects.

You can use the following general syntax to both conﬁgure and estimate

process models:

m = pem(data,mod_struc,'Property1',Value1,...,

'PropertyN',ValueN)

data

is th e estimation data and mod_s truc is a string that represents

the process model structure, as described in “Options for Specifying the

Process-Model Structure” on page 3-35.

Tip You do not need to construct the model object u sing idproc before

estimation unless you want to specify initial parameter guesses or ﬁxed

parameter values, as d escribed in “Example – Estimating Pro cess Models

with Fixed Parameters at the Command Line” on page 3 -32.

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.

3-29