User`s guide

Identifying Nonlinear ARX Models

For detailed information about the nlarx and idnlarx properties and values,

see the corresponding reference page.

For mo re information about validating models, see Chapter 8, “Model

Analysis”.

Note You do not need to construct the model object using idnlarx befo re

estimation.

You can also use pem to reﬁne parameter estimates of an existing nonlinear

ARX m odel, as described in “Reﬁning Nonlinear Black-Box Models” on page

4-28.

Example – Using nlarx to Estimate Nonlinear ARX Models

This example uses nlarx to estimate a nonlinear AR X model for the two-tank

system. The data for this system is described in “Tutorial – Identifying

Nonlinear Bl ack - B ox M odels U s i ng the GU I” in the System Identiﬁcation

Toolbox Getting Started G uide.

Prepare th e d ata for estimation u sin g the following commands:

load twotankda ta

z = iddata(y, u, 0.2);

ze = z(1:1000); zv = z(10 01:3 000);

Estimate several m odels using diff erent model orders, delays, and

nonlinearity settings:

m1 = nlarx(ze,[2 2 1],'wav');

m2 = nlarx(ze,[2 2 3],wavenet);

m3 = nlarx(ze,[2 2 3],wavenet('num',8));

m4 = nlarx(ze,[2 2 3],wavenet('num',8),...

'nlr', [1 2]);

m5 = nlarx(ze,[2 2 3],sigmoidnet('num',14),...

'nlr',[1 2]);

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