User`s guide

Identifying Input-Output Polynomial Models

The A RX and IV algorithms treat noise d ifferently. ARX assumes white noise.

However, the instrumental variable algorithm, IV, is not sensitive to noise

color. Thus, u se IV when the noise in your system is not completely w hite and

it is incorrect to assume white noise. If the models you obtained using ARX

are inaccurate, try using IV.

Note AR models apply to time-series data, which has no input. For more

information, see Chapter 6, “Time Series Model Identiﬁcation”. For more

information about working w ith AR and AR X models, see “Identifying

Input-Output Polynomial Models” on page 3-41.

Example – Estimating Models Using armax

You can use estimation commands to both construct a m odel object and

estimate the m odel param eters. In this example, you estimate a linear,

polynomial model with an ARMAX structure for a three-input and

single-output (MISO) system using the iterative estimation method

armax.

For a summary of all available estimation commands in the toolbox, see

“Commands for Model Estimation” on page 2-9.

1 Load a sample data set z8 with three inputs and one output, measured at

1-second intervals and containing 500 data samples:

load iddata8

2 Use armax to both construct the idp oly model object, and estimate the

parameters:

Aqyt B qu t nk Cqet

ii i

()() () ()()=−

()

∑

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