Main Content

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`

)

`[`

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.

`oeOptions`

| `tfest`

| `arx`

| `armax`

| `iv4`

| `n4sid`

| `bj`

| `polyest`

| `idpoly`

| `iddata`

| `idfrd`

| `sim`

| `compare`