Documentation

# polyreg

Powers and products of standard regressors

## Syntax

```R = polyreg(model) R = polyreg(model,'MaxPower',n) R = polyreg(model,'MaxPower',n,'CrossTerm',CrossTermVal) ```

## Description

`R = polyreg(model)` creates an array `R` of polynomial regressors up to the power 2. If a model order has input `u` and output `y`, `na`=`nb`=2, and delay `nk`=1, polynomial regressors are y(t−1)2, u(t−1)2, y(t−2)2, u(t−2)2. `model` is an `idnlarx` object. You must add these regressors to the `model` by assigning the `CustomRegressors` `model` property or by using `addreg`.

`R = polyreg(model,'MaxPower',n)` creates an array `R` of polynomial regressors up to the power `n`. Excludes terms of power `1` and cross terms, such as y(t−1)*u(t−1).

`R = polyreg(model,'MaxPower',n,'CrossTerm',CrossTermVal)` creates an array `R` of polynomial regressors up to the power `n` and includes cross terms (products of standards regressors) when `CrossTermVal` is `'on'`. By default, `CrossTermVal` is `'off'`.

## Examples

collapse all

Estimate a nonlinear ARX model with $na=2$, $nb=2$, and $nk=1$, and nonlinearity estimator `wavenet`.

```load iddata1 m = nlarx(z1,[2 2 1]);```

Create polynomial regressors.

`R = polyreg(m);`

Estimate the model.

`m = nlarx(z1,[2 2 1],'wavenet','CustomReg',R);`

View all model regressors (standard and custom).

`getreg(m)`
```Regressors: y1(t-1) y1(t-2) u1(t-1) u1(t-2) y1(t-1).^2 y1(t-2).^2 u1(t-1).^2 u1(t-2).^2 ```

Estimate a nonlinear ARX model with $na=2$, $nb=1$, and $nk=1$, and nonlinearity estimator `wavenet`.

```load iddata1 m = nlarx(z1,[2 1 1]);```

Create polynomial regressors.

`R = polyreg(m,'MaxPower',3,'CrossTerm','on')`
```16x1 array of Custom Regressors with fields: Function, Arguments, Delays, Vectorized ```

If the model `m` has three standard regressors `a`, `b` and `c`, then `R` includes the terms ${a}^{2}$, ${b}^{2}$, ${c}^{2}$, $ab$, $ac$, $bc$, ${a}^{2}b$, ${a}^{2}c$, $a{b}^{2}$, $abc$, $a{c}^{2}$, ${b}^{2}c$, $b{c}^{2}$, ${a}^{3}$, ${b}^{3}$, and ${c}^{3}$.

Estimate the model.

`m = nlarx(z1,[2 1 1],'wavenet','CustomReg',R);`