Estimate output-error polynomial model using time-domain or frequency-domain data
Output-error (OE) models are a special configuration of polynomial models, having
only two active polynomials—B and F. OE models represent
conventional transfer functions that relate measured inputs to outputs while also including
white noise as an additive output disturbance. You can estimate OE models using time- and
frequency-domain data. The tfest
command offers the same functionality as
oe
. For tfest
, you specify the model orders using
number of poles and zeros rather than polynomial degrees. For continuous-time estimation,
tfest
provides faster and more accurate results, and is
recommended.
estimates an OE model sys
= oe(data
,[nb
nf nk]
)sys
, represented by
y(t) is the output, u(t) is the input, and e(t) is the error.
oe
estimates sys
using the measured
input-output data data
, which can be in the time or the frequency
domain. The orders [nb nf nk]
define the number of parameters in each
component of the estimated polynomial.
specifies model structure attributes using additional options specified by one or more
name-value pair arguments.sys
= oe(data
,[nb
nf nk]
,Name,Value
)
[
returns the estimated initial conditions as an sys
,ic
] = oe(___)initialCondition
object. Use this syntax if you plan to simulate or predict the model response using the same
estimation input data and then compare the response with the same estimation output data.
Incorporating the initial conditions yields a better match during the first part of the
simulation.
armax
| arx
| bj
| compare
| iddata
| idfrd
| idpoly
| iv4
| n4sid
| oeOptions
| polyest
| sim
| tfest