User`s guide

3 Linear Model Identification
method gives the maximum likelihood estimate. that minimizes the prediction
errors to obtain maximum-likelihood values. The re sulting mode ls are stored
as
idpoly model objects.
Use the following general syntax to both configure and estimate polynomial
models:
m = pem(data,'na',na,
'nb',nb,
'nc',nc,
'nd',nb,
'nf',nc,
'nk',nk,
'Property1',Value1,...,
'PropertyN',ValueN)
where data is the estimation data. na, nb, nc, nd, nf are integers that specify
the model orders, and
nk specifies the input delays for each input. If you
skip any property-value pair, the corresponding parameter value is set to
zero—except
nk, which has the default value 1. For m ore information about
model orders, see “What Are Black-Box Polynomial M odels?” on page 3-41.
Tip You do not need to construct the model object using idoly before
estimation.
If you want to estimate the coefficients of all five polynomials, A, B, C, D,and
F, you must specify an integer order for each polynomial. However, if y ou
want to specify an ARMAX m odel for example, which includes only the A, B,
and C polynomials, you must set
nd and nf to 0.
Note To get faster estimation of ARX models, use arx or iv4 instead of pem.
In addition to the polynomial models listed in “Wh at A re Black-Bo x
Polynomi al Mod els?” on pag e 3-41, you ca n use
pem to model the ARA RX
structure—called the generalized l east-squares m odel— by setting
nc=nf=0.
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