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

$$y(t)=\frac{B(q)}{F(q)}u(t-nk)+e(t)$$

*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`

)

`armax`

| `arx`

| `bj`

| `compare`

| `iddata`

| `idfrd`

| `idpoly`

| `iv4`

| `n4sid`

| `oeOptions`

| `polyest`

| `sim`

| `tfest`