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

Create Polynomial Regressors Up To Power 2

Open Live Script

Estimate a nonlinear ARX model with $n a = 2$ , $n b = 2$ , and $n k = 1$ , 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

Create Polynomial Regressors Up To Power 3

Open Live Script

Estimate a nonlinear ARX model with $n a = 2$ , $n b = 1$ , and $n k = 1$ , 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 $a^{2}$ , $b^{2}$ , $c^{2}$ , $a b$ , $a c$ , $b c$ , $a^{2} b$ , $a^{2} c$ , $a b^{2}$ , $a b c$ , $a c^{2}$ , $b^{2} c$ , $b c^{2}$ , $a^{3}$ , $b^{3}$ , and $c^{3}$ .

Estimate the model.

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

Documentation

polyreg

Syntax

Description

Examples

Create Polynomial Regressors Up To Power 2

Create Polynomial Regressors Up To Power 3

See Also

Topics

System Identification Toolbox Documentation

Support