polynomialRegressor

Specify polynomial regressor for nonlinear ARX model

Description

Polynomial regressors are polynomials that are composed of delayed input and output variables. For example, y(t–1)2 and y(t–1) u(t–1) are both polynomial regressors with orders of 2 and variable delays of one sample. A polynomialRegressor object encapsulates a set of polynomial regressors. Use polynomialRegressor objects when you create nonlinear ARX models using idnlarx or nlarx. You can specify polynomialRegressor objects along with linearRegressor and customRegressor objects and combine them into a single combined regressor set.

Creation

Description

example

pReg = polynomialRegressor(Variables,Lags) creates a polynomialRegressor object of order 2 that contains output and input names in Variables and the corresponding lags in Lags. For example, if Variables contains 'y' and lags contains the corresponding lag vector [2 4], then the regressors that use 'y' are y(t–2)2 and y(t–4)2.

example

pReg = polynomialRegressor(Variables,Lags,Order) creates a polynomialRegressor object of order Order .

example

pReg = polynomialRegressor(Variables,Lags,Order,UseAbsolute) specifies in UseAbsolute whether to use the absolute values of the variables to create the regressors.

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pReg = polynomialRegressor(Variables,Lags,Order,UseAbsolute,AllowVariableMix) specifies in AllowVariableMix whether to allow multiple variables in the regressor formulas. For example, if Variables is equal to {'y','u'}, Lags is equal to {1,1}, and Order is equal to 2, then a value of true for AllowVariableMix results in the inclusion of the mixed-variable regressor y(t–1)u(t–1), along with the single-variable regressors y(t–1)2 and u(t–1)2.

example

pReg = polynomialRegressor(Variables,Lags,Order,UseAbsolute,AllowVariableMix,AllowLagMix) specifies in AllowLagMix whether to allow different lags in the regressor formulas. For example, if Variables is equal to {'y','u'}, Lags is equal to {2,[0 3]}, Order is equal to 2, and AllowVariableMix is equal to false, then a value of true for AllowLagMix results in the inclusion of the mixed-lag regressor u(t)u(t–3), along with the unique-lag regressors y(t–2)2, u(t)2, and u(t–3)2. Note that if you set AllowVariableMix to true, then the regressor set will also include y(t–2)u(t) and y(t–2)u(t–3).

Properties

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Output and input variable names, specified as a cell array of strings or a cell array that references the OutputName and InputName properties of an iddata object. Each entry must be a string with no special characters other than white space. For an example of using this property, see Estimate Nonlinear ARX Model with Polynomial Regressors.

Example: {'y1','u1'}

Example: [z.OutputName; z.InputName]'

Lags in each variable, specified as a 1-by-nv cell array of non-negative integer row vectors, where nv is the total number of regressor variables. Each row vector contains nr integers that specify the nr regressor lags for the corresponding variable. For instance, suppose that you want the following regressors:

• Output variable y1: y1(t–1)2 and y1(t–2)2

• Input variable u1: u1(t–3)2

To obtain these lags, set Lags to {[1 2],3}.

If a lag corresponds to an output variable of an idnlarx model, the minimum lag must be greater than or equal to 1.

For an example of using this property, see Estimate Nonlinear ARX Model with Polynomial Regressors.

Example: {1 1}

Example: {[1 2],[1,3,4]}

Absolute value indicator that determines whether to use the absolute value of a regressor variable instead of the signed value, specified as a logical vector with a length equal to the number of variables.

For an example of setting this property, see Use Absolute Value in Polynomial Regressor Set.

Example: [true,false]

Mixed variables indicator that determines whether to use multiple variables in regressor formulas such as y(t–1)u(t–1), specified as a logical vector with a length equal to the number of variables.

For an example of setting this property, see Use Multiple Variables in Polynomial Regressor Term.

Example: [true,false]

Mixed lag indicator that determines whether to use different lags in regressor formulas such as u(t)u(t–3), specified as a logical vector with a length equal to the number of variables.

To set this property for an existing nonlinear ARX model sys, use dot notation, as shown in the following command.

For an example of setting this property, see Use Mixed Lags in Polynomial Regressor Term.

Example: [true,false]

Name of the time variable, specified as a valid MATLAB® variable name that is distinct from values in Variables.

Example: 'ClockTime'

Examples

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Load the data and create an iddata object z with a sample time of 0.1 seconds.

z = iddata(y,u,'Ts',0.1);

Specify polynomial regressors that have the forms ${\mathit{u}\left(\mathit{t}-2\right)}^{2}$, ${\mathit{u}\left(\mathit{t}-4\right)}^{2}$, and ${\mathit{y}\left(\mathit{t}-1\right)}^{2}$.

Use the properties of z to specify the variable names.

Variables = [z.OutputName;z.InputName];

Specify the lags.

Lags = {1,[2 4]};

Create the regressor. The default order is 2.

pReg = polynomialRegressor(Variables,Lags)
pReg =
Order 2 regressors in variables y1, u1
Order: 2
Variables: {'y1'  'u1'}
Lags: {[1]  [2 4]}
UseAbsolute: [0 0]
AllowVariableMix: 0
AllowLagMix: 0
TimeVariable: 't'

Regressors described by this set

Use pReg to estimate the nonlinear ARX model.

sys = nlarx(z,pReg)
sys =
Nonlinear ARX model with 1 output and 1 input
Inputs: u1
Outputs: y1

Regressors:
Order 2 regressors in variables y1, u1
List of all regressors

Output function: Wavelet network with 66 units
Sample time: 0.1 seconds

Status:
Estimated using NLARX on time domain data "z".
Fit to estimation data: 95.97% (prediction focus)
FPE: 5.843e-05, MSE: 5.569e-05

View the regressors.

getreg(sys)
ans = 3x1 cell
{'y1(t-1)^2'}
{'u1(t-2)^2'}
{'u1(t-4)^2'}

Specify the third-order polynomial regressor ${{\mathit{u}}_{1}\left(\mathit{t}-2\right)}^{3}$.

Variables = 'u1';
Lags = 2;
Order = 3;
pReg = polynomialRegressor(Variables,Lags,Order)
pReg =
Order 3 regressors in variables u1
Order: 3
Variables: {'u1'}
Lags: {[2]}
UseAbsolute: 0
AllowVariableMix: 0
AllowLagMix: 0
TimeVariable: 't'

Regressors described by this set

Create a second-order polynomial regressor set that uses lags of 3, 10, and 100 in variable y1 and lags of 0 and 4 in variable u1.

vars = {'y1','u1'};
lags = {[3 10 100],[0,4]};

Specify that the y1 regressor use the absolute value of y1.

UseAbs = [true,false];

Create the polynomial regressor.

reg = polynomialRegressor(vars,lags,2,UseAbs)
reg =
Order 2 regressors in variables y1, u1
Order: 2
Variables: {'y1'  'u1'}
Lags: {[3 10 100]  [0 4]}
UseAbsolute: [1 0]
AllowVariableMix: 0
AllowLagMix: 0
TimeVariable: 't'

Regressors described by this set

Create a polynomial regressor set that includes the terms ${{\mathit{y}}_{1}\left(\mathit{t}-1\right)}^{2}$, ${{\mathit{u}}_{1}\left(\mathit{t}-1\right)}^{2}$, and ${\mathit{y}}_{1}\left(\mathit{t}-1\right){\mathit{u}}_{1}\left(\mathit{t}-1\right)$.

Specify the variables and lags.

vars = {'y1','u1'};
lags = {1, 1};

Specify that mixed-variable regressors be created.

mixvar = true;

Create a second-order polynomial regressor using mixvar. Set the fourth position, which represents the UseAbsolute property, to false.

reg = polynomialRegressor(vars,lags,2,false,mixvar)
reg =
Order 2 regressors in variables y1, u1
Order: 2
Variables: {'y1'  'u1'}
Lags: {[1]  [1]}
UseAbsolute: [0 0]
AllowVariableMix: 1
AllowLagMix: 0
TimeVariable: 't'

Regressors described by this set

As an alternative, you can create the regressor specification first using the variables and lags and set the AllowVariableMix property afterward using dot notation.

reg1 = polynomialRegressor(vars,lags);
reg1.AllowVariablemix = true
reg1 =
Order 2 regressors in variables y1, u1
Order: 2
Variables: {'y1'  'u1'}
Lags: {[1]  [1]}
UseAbsolute: [0 0]
AllowVariableMix: 1
AllowLagMix: 0
TimeVariable: 't'

Regressors described by this set

Use reg1 in a nonlinear ARX model.

z = iddata(y,u,'Ts',0.1);
sys = nlarx(z,reg1);

View the regressors.

getreg(sys)
ans = 3x1 cell
{'y1(t-1)^2'      }
{'u1(t-1)^2'      }
{'y1(t-1)*u1(t-1)'}

The regressors include mixed-variable terms.

Specify a polynomial regressor set that includes a term of the form $\mathit{u}\left(\mathit{t}\right)$$\mathit{u}\left(\mathit{t}-3\right)$.

Specify the variable names and the lags.

vars = {'y1','u1'};
lags = {2,[0 3]};

Initialize a second-order polynomial regressor.

reg = polynomialRegressor(vars,lags);

Specify that the regressor use mixed lags.

reg.AllowLagMix = true;

Use the regressor set in a nonlinear ARX model.

z = iddata(y,u,'Ts',0.1);
sys = nlarx(z,reg);

View the regressors.

getreg(sys)
ans = 4x1 cell
{'y1(t-2)^2'    }
{'u1(t)^2'      }
{'u1(t-3)^2'    }
{'u1(t)*u1(t-3)'}

The regressors include the mixed-lag term.

Load the data and create an iddata object z.

z = iddata(y,u,'Ts',0.1);

Specify polynomial regressors that have the forms ${\mathit{u}\left(\mathit{t}-2\right)}^{2}$ and ${\mathit{u}\left(\mathit{t}-4\right)}^{2}$. Also specify a linear regressor of the form $\mathit{y}\left(\mathit{t}-1\right)$.

Specify the input lag.

uLags = {[2 4]};

Specify the polynomial regressors. The default regressor order is 2.

pReg = polynomialRegressor(z.InputName,uLags);

Specify the output lag and specify the linear regressor.

lLags = 1;
lReg = linearRegressor(z.OutputName,lLags);

Estimate a nonlinear ARX model.

reg = [pReg;lReg]
reg =
[2 1] array of polynomialRegressor, linearRegressor objects.
------------------------------------
1. Order 2 regressors in variables u1
Order: 2
Variables: {'u1'}
Lags: {[2 4]}
UseAbsolute: 0
AllowVariableMix: 0
AllowLagMix: 0
TimeVariable: 't'

------------------------------------
2. Linear regressors in variables y1
Variables: {'y1'}
Lags: {[1]}
UseAbsolute: 0
TimeVariable: 't'

Regressors described by this set
sys = nlarx(z,reg)
sys =
Nonlinear ARX model with 1 output and 1 input
Inputs: u1
Outputs: y1

Regressors:
1. Linear regressors in variables y1
2. Order 2 regressors in variables u1
List of all regressors

Output function: Wavelet network with 21 units
Sample time: 0.1 seconds

Status:
Estimated using NLARX on time domain data "z".
Fit to estimation data: 96.56% (prediction focus)
FPE: 4.133e-05, MSE: 4.059e-05

View the regressors.

getreg(sys)
ans = 3x1 cell
{'u1(t-2)^2'}
{'u1(t-4)^2'}
{'y1(t-1)'  }

Load the estimation data z1, which has one input and one output, and obtain the output and input names.

names = [z1.OutputName z1.InputName]
names = 1x2 cell
{'y1'}    {'u1'}

Specify L as the set of linear regressors that represents ${\mathit{y}}_{1}\left(\mathit{t}-1\right)$, ${\mathit{u}}_{1}\left(\mathit{t}-2\right)$, and ${\mathit{u}}_{1}\left(\mathit{t}-5\right)$.

L = linearRegressor(names,{1,[2 5]});

Specify P as the polynomial regressor ${{\mathit{y}}_{1}\left(\mathit{t}-1\right)}^{2}$.

P = polynomialRegressor(names(1),1,2);

Specify C as the custom regressor ${\mathit{y}}_{1}\left(\mathit{t}-2\right)$${\mathit{u}}_{1}\left(\mathit{t}-3\right)$. Use an anonymous function handle to define this function.

C = customRegressor(names,{2 3},@(x,y)x.*y)
C =
Custom regressor: y1(t-2).*u1(t-3)
VariablesToRegressorFcn: @(x,y)x.*y
Variables: {'y1'  'u1'}
Lags: {[2]  [3]}
Vectorized: 1
TimeVariable: 't'

Regressors described by this set

Combine the regressors in the column vector R.

R = [L;P;C]
R =
[3 1] array of linearRegressor, polynomialRegressor, customRegressor objects.
------------------------------------
1. Linear regressors in variables y1, u1
Variables: {'y1'  'u1'}
Lags: {[1]  [2 5]}
UseAbsolute: [0 0]
TimeVariable: 't'

------------------------------------
2. Order 2 regressors in variables y1
Order: 2
Variables: {'y1'}
Lags: {[1]}
UseAbsolute: 0
AllowVariableMix: 0
AllowLagMix: 0
TimeVariable: 't'

------------------------------------
3. Custom regressor: y1(t-2).*u1(t-3)
VariablesToRegressorFcn: @(x,y)x.*y
Variables: {'y1'  'u1'}
Lags: {[2]  [3]}
Vectorized: 1
TimeVariable: 't'

Regressors described by this set

Estimate a nonlinear ARX model with R.

sys = nlarx(z1,R)
sys =
Nonlinear ARX model with 1 output and 1 input
Inputs: u1
Outputs: y1

Regressors:
1. Linear regressors in variables y1, u1
2. Order 2 regressors in variables y1
3. Custom regressor: y1(t-2).*u1(t-3)
List of all regressors

Output function: Wavelet network with 1 units
Sample time: 0.1 seconds

Status:
Estimated using NLARX on time domain data "z1".
Fit to estimation data: 59.73% (prediction focus)
FPE: 3.356, MSE: 3.147

View the full regressor set.

getreg(sys)
ans = 5x1 cell
{'y1(t-1)'         }
{'u1(t-2)'         }
{'u1(t-5)'         }
{'y1(t-1)^2'       }
{'y1(t-2).*u1(t-3)'}