User`s guide

4 Nonlinear Black-Box Model Identification
General nlarx Syntax
You can estimate nonlinear ARX models using nlarx. The resulting models
are stored as
idnlarx model objects.
Use the following general syntax to both configure and estimate nonlinear
ARX models:
m = nlarx(data,'na',na,...
'nb',nb,...
'nk',nk,...
Nonlinearity,...
'Property1',Value1,...,
'PropertyN',ValueN)
where data is the estimation data. na, nb,andnk specify the mode l orders
and delays. For more information about model orders, see “Specifying Model
Order and Delays” on page 4-6.
Nonlinearity specifies the nonlinearity e stimator object as 'sigmoidn et',
'wavenet', 'treepart itio n', 'cus tom net', 'neura lnet ',or'lin ear '.
The property-value pairs specify any
idnlarx model properties that configure
the estimation algorithm. You can enter all model property-value pairs and
top-level algorith m properties as a c omma-separated list in
nlarx.
For multiple inputs a nd outputs,
na, nb,andnk are described in “Options for
Multiple-Input and Multiple-Output ARX Orders” on page 3-64.
You can specify different nonlinearity estimators for different output channels
by setting
Nonlinearity to an object array. For example:
m = nlarx(data,[[2 1; 0 1 ] [2;1] [1;1]],...
[wavenet;sigmoidnet('num',7)])
To specify the same nonlinearity for all outputs, set Nonlinearity to a single
nonlinearity estimator. For example:
m = nlarx(data,[[2 1; 0 1 ] [2;1] [1;1]],...
sigmoidnet('num',7))
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