Estimate polynomial model using time or frequencydomain data
sys = polyest(data,[na nb nc nd nf nk])
sys = polyest(data,[na nb nc nd nf nk],Name,Value)
sys = polyest(data,init_sys)
sys = polyest(___, opt)
estimates
a polynomial model, sys
= polyest(data
,[na
nb
nc
nd
nf
nk
])sys
,
using the time or frequencydomain data, data
.
sys
is of the form
$$A(q)y(t)=\frac{B(q)}{F(q)}u(tnk)+\frac{C(q)}{D(q)}e(t)$$
A(q), B(q), F(q), C(q)
and D(q) are polynomial matrices. u(t)
is the input, and nk
is the input delay. y(t)
is the output and e(t) is the
disturbance signal. na
,nb
, nc
, nd
and nf
are
the orders of the A(q), B(q), C(q), D(q)
and F(q) polynomials, respectively.
estimates
a polynomial model with additional attributes of the estimated model
structure specified by one or more sys
= polyest(data
,[na
nb
nc
nd
nf
nk
],Name,Value
)Name,Value
pair
arguments.
estimates
a polynomial model using the dynamic system sys
= polyest(data
,init_sys
)init_sys
to
configure the initial parameterization.
estimates
a polynomial model using the option set, sys
= polyest(___, opt
)opt
,
to specify estimation behavior.

Estimation data. For timedomain estimation, You can estimate only discretetime models using timedomain
data. For estimating continuoustime models using timedomain data,
see For frequencydomain estimation,


Order of the polynomial A(q).


Order of the polynomial B(q) + 1.


Order of the polynomial C(q).


Order of the polynomial D(q).


Order of the polynomial F(q).


Input delay in number of samples, expressed as fixed leading zeros of the B polynomial.


Estimation options.


Dynamic system that configures the initial parameterization
of If If Use the To specify an initial guess for, say, the A(q)
term of To specify constraints for, say, the B(q)
term of
You can similarly specify the initial guess and constraints for the other polynomials. If 
Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.

Transport delays. For continuoustime systems, specify transport delays in the
time unit stored in the For a MIMO system with Default: 

Input delay for each input channel, specified as a scalar value
or numeric vector. For continuoustime systems, specify input delays
in the time unit stored in the For a system with You can also set Default: 0 

Logical vector specifying integrators in the noise channel.
Setting $$A(q)y(t)=\frac{B(q)}{F(q)}u(tnk)+\frac{C(q)}{D(q)}\frac{e(t)}{1{q}^{1}}$$ Where, $$\frac{1}{1{q}^{1}}$$ is the integrator in the noise channel, e(t). Use For example, load iddata1 z1; z1 = iddata(cumsum(z1.y),cumsum(z1.u),z1.Ts,'InterSample','foh'); sys = polyest(z1, [2 2 2 0 0 1],'IntegrateNoise',true); 

Estimated polynomial model.
If $$Y(s)=\frac{B(s)}{F(s)}U(s)+E(s)$$ Y(s), U(s) and E(s) are the Laplace transforms of the timedomain signals y(t), u(t) and e(t), respectively. 
To estimate a polynomial model using timeseries data,
use ar
.
Use polyest
to estimate a polynomial
of arbitrary structure. If the structure of the estimated polynomial
model is known, that is, you know which polynomials will be active,
then use the appropriate dedicated estimating function. For examples,
for an ARX model, use arx
. Other
polynomial model estimating functions include, oe
, armax
, and bj
.
To estimate a continuoustime transfer function, use tfest
. You can also use oe
, but only with continuoustime frequencydomain
data.