# idWaveletNetwork

Wavelet network function for nonlinear ARX and Hammerstein-Wiener models

## Description

An idWaveletNetwork object implements a wavelet network function, and is a nonlinear mapping function for estimating nonlinear ARX and Nonlinear Hammerstein-Wiener models. The mapping function, which is also referred to as a nonlinearity, uses a combination of linear weights, an offset and a nonlinear function to compute its output. The nonlinear function contains wavelet unit functions that operate on a radial combination of inputs.

Mathematically, idWaveletNetwork is a function that maps m inputs X(t) = [x(t1),x2(t),…,xm(t)]T to a scalar output y(t) using the following relationship:

$y\left(t\right)={y}_{0}+Χ{\left(t\right)}^{T}PL+W\left(Χ\left(t\right)\right)+S\left(Χ\left(t\right)\right)$

Here:

• X(t) is an m-by-1 vector of inputs, or regressors.

• y0 is the output offset, a scalar.

• P is an m-by-p projection matrix, where m is the number of regressors and is p is the number of linear weights. m must be greater than or equal to p.

• L is a p-by-1 vector of weights.

• W(X) and S(X) together constitute the nonlinear function of the wavelet network. W(X) is a sum of dilated and translated wavelets while S(X) is a sum of dilated and translated scaling functions (also known as scalelets). The total number of wavelet dw and scaling functions ds is referred to as the number of units of the network.

For definitions of the wavelet function term W(X) and the scaling function term S(X), see More About.

Use idWaveletNetwork as the value of the OutputFcn property of an idnlarx model or the InputNonlinearity and OutputLinearity properties of an idnlhw object. For example, specify idWaveletNetwork when you estimate an idnlarx model with the following command.

sys = nlarx(data,regressors,idWaveletNetwork)
When nlarx estimates the model, it essentially estimates the parameters of the idWaveletNetwork function.

You can configure the idWaveletNetwork function to disable components and fix parameters. To omit the linear component, set LinearFcn.Use to false. To omit the offset, set Offset.Use to false. To specify known values for the linear function and the offset, set their Value attributes directly and set the corresponding Free attributes to False. Use evaluate to compute the output of the function for a given vector of inputs.

## Creation

### Description

example

W = idWaveletNetwork creates a idWaveletNetwork object W, for which the function computes the number of units automatically during model estimation.

example

W = idWaveletNetwork(numUnits) specifies the number of units numUnits. This syntax includes an option that allows you to interactively assess the relationship between the number of units and unexplained variance.

example

W = idWaveletNetwork(numUnits,UseLinearFcn) specifies whether the function uses a linear function as a subcomponent.

example

W = idWaveletNetwork(numUnits,UseLinearFcn,UseOffset) specifies whether the function uses an offset term.

### Input Arguments

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Number of units, specified as the string or character vector that represents 'auto' or 'interactive', or as a positive integer. numUnits determines the number of wavelets or scaling functions, or, if both elements are present, the combined number of wavelets and scaling functions. Typically, the wavelet network contains either wavelets or scaling functions, but not both. Specify numUnits as one of the following values:

• 'auto' — The software determines the number of units automatically during model estimation.

• 'interactive' — During model estimation, the software displays an interactive bar plot that relates unexplained variance to the number of units. Click on a bar to view the achievable fit to the estimation data for the selected number of units. A blue bar indicates the optimal choice, based on the generalized cross-validation (GCV) criterion. A general rule for the selection of the number of units is to use the smallest number of units that capture most of the variance.

• Positive integer — The software uses the specified value directly.

This argument sets the W.NonlinearFcn.NumberOfUnits property.

Option to use the linear function subcomponent, specified as true or false. This argument sets the value of the W.LinearFcn.Use property.

Option to use an offset term, specified as true or false. This argument sets the value of the W.Offset.Use property.

## Properties

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Input signal names for the inputs to the mapping object, specified as a 1-by-m cell array, where m is the number of input signals. This property is determined during estimation.

Output signal name for the output of the mapping object, specified as a 1-by-1 cell array. This property is determined during estimation.

Parameters of the linear function, specified as follows:

• Use — Option to use the linear function in the wavelet network, specified as a scalar logical. The default value is true. For an example of setting this option, see Exclude Linear Term from Wavelet Network Mapping Object.

• Value — Linear weights that compose L', specified as a 1-by-p vector.

• InputProjection — Input projection matrix P, specified as an m-by-p matrix, that transforms the input vector of length m into a vector of length p. For Hammerstein-Wiener models, InputProjection is equal to 1.

• Free — Option to update entries of Value during estimation, specified as a 1-by-p logical vector. The software honors the Free specification only if the starting value of Value is finite. The default value is true.

Parameters of the offset term, specified as follows:

• Use — Option to use the offset in the wavelet network, specified as a scalar logical. The default value is true.

• Value — Offset value, specified as a scalar.

• Free — Option to update Value during estimation, specified as a scalar logical. The software honors the Free specification of false only if the value of Value is finite. The default value is true.

Parameters of the nonlinear function, specified as follows:

• NumberOfUnits — Number of units, specified as 'auto', 'interactive', or a positive integer. NumberOfUnits determines the number of wavelets or scaling functions, or, if both elements are present, the combined number of wavelets and scaling functions. Typically, the wavelet network contains either wavelets or scaling functions, but not both. The options for NumberOfUnits are as follows:

• 'auto' — The software determines the number of units automatically during model estimation.

• 'interactive' — During model estimation, the software displays an interactive bar plot that relates unexplained variance to the number of units. Click on a bar to view the achievable fit to the estimation data for the selected number of units. A blue bar indicates the optimal choice, based on the generalized cross-validation (GCV) criterion. A general rule for the selection of the number of units is to use the smallest number of units that capture most of the variance.

• Positive integer — The software uses the specified value directly.

• Structure — Advanced options that control the structure of the wavelet and scaling functions, specified as in the following table.

PropertyDescriptionDefault
FinestCellMinimum number of data points in the smallest cell, specified as 'auto' or a positive integer. A cell is the area covered by the part of a wavelet that is significantly nonzero. The default setting of 'auto' specifies that the software determines this value during estimation.'auto'
MinimumCellsMinimum number of cells in the partition, specified as a positive integer.16
MaximumCellsMaximum number of cells in the partition, specified as a positive integer.16
MaximumLevelsMaximum number of wavelet levels, specified as a positive integer. 10
DilationStepDilation step size, specified as a positive integer.2
TranslationStepTranslation step size, specified as a positive integer.1

• Parameters — Parameters of idWaveletNetwork, specified as in the following table.

Field NameDescriptionDefault
InputProjection

Projection matrix Q, specified as an m-by-q matrix. Q transforms the detrended input vector $\left(X-\overline{X}\right)$ of length m into a vector of length q. Typically, Q has the same dimensions as the linear projection matrix P. In this case, q is equal to p, which is the number of linear weights.

For Hammerstein-Wiener models, InputProjection is equal to 1.

[]
ScalingCoefficient

Scaling function coefficients si, specified as a ds-by-1 vector.

[]
ScalingTranslation

Scaling translation matrix, specified as a ds-by-q matrix of scaling translation row vectors ei.

[]
ScalingDilation

Scaling function dilation coefficients di, specified as an ds-by-1 vector.

[]
WaveletCoefficient

Wavelet function coefficients wi, specified as a dw-by-1 vector.

[]
WaveletTranslation

Wavelet translation matrix, ei, specified as a dw-by-q matrix of wavelet translation row vectors ci.

[]
WaveletDilation

Wavelet dilation coefficients bi, specified as an dw-by-1 vector.

[]

• Free — Option to estimate parameters, specified as a logical scalar. If all the parameters have finite values, such as when the idWaveletNetwork object corresponds to a previously estimated model, then setting Free to false causes the parameters of the nonlinear functions W(X) and S(X) to remain unchanged during estimation. The default value is true.

## Examples

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MO = idWaveletNetwork;

View the idWaveletNetwork object.

disp(MO)
Wavelet Network

Nonlinear Function: Wavelet network with number of units chosen automatically
Linear Function: uninitialized
Output Offset: uninitialized

Inputs: {1x0 cell}
Outputs: {1x0 cell}
NonlinearFcn: '<Wavelet and scaling function units and their parameters>'
LinearFcn: '<Linear function parameters>'
Offset: '<Offset parameters>'
EstimationOptions: '<Estimation options>'

Create the idWaveletNetwork mapping object MO.

MO = idWaveletNetwork;

Exclude the linear term from MO.

MO.LinearFcn.Use = false;

View the idWaveletNetwork object.

disp(MO)
Wavelet Network

Nonlinear Function: Wavelet network with number of units chosen automatically
Linear Function: not in use
Output Offset: uninitialized

Inputs: {1x0 cell}
Outputs: {1x0 cell}
NonlinearFcn: '<Wavelet and scaling function units and their parameters>'
LinearFcn: '<Linear function parameters>'
Offset: '<Offset parameters>'
EstimationOptions: '<Estimation options>'

The linear function is not in use.

Create an iddata object from the estimation data.

z = iddata(y,u,0.2);

Create a wavelet network mapping object with five units.

MO = idWaveletNetwork(5);

Estimate the nonlinear ARX model.

sys = nlarx(z,[4 4 1],MO)
sys =
Nonlinear ARX model with 1 output and 1 input
Inputs: u1
Outputs: y1

Regressors:
Linear regressors in variables y1, u1
List of all regressors

Output function: Wavelet network with 5 units
Sample time: 0.2 seconds

Status:
Estimated using NLARX on time domain data "z".
Fit to estimation data: 96.8% (prediction focus)
FPE: 3.553e-05, MSE: 3.515e-05

Create an iddata object.

z = iddata(y,u,0.02,'Name','Motorized Camera','TimeUnit','s');

z is an iddata object with six inputs and two outputs.

Specify the model orders and delays.

Orders = [ones(2,6),ones(2,6),ones(2,6)];

Specify the same nonlinearity estimator for each input channel.

InputNL = idWaveletNetwork;

Specify different nonlinearity estimators for each output channel.

Estimate the Hammerstein-Wiener model.

sys = nlhw(z,Orders,InputNL,OutputNL);

To see the shape of the estimated input and output nonlinearities, plot the nonlinearities.

plot(sys)

Click on the input and output nonlinearity blocks on the top of the plot to see the nonlinearities.

Specify idWaveletNetwork for the model output function. Configure the idWaveletNetwork object to have a fixed offset value of 1 and to use five units in the nonlinear component.

w = idWaveletNetwork;
w.Offset.Value = 1;
w.Offset.Free = false;
w.NonlinearFcn.NumberOfUnits = 5;

Specify a linear model regressor set using a lag array that produces four consecutive output regressors and five consecutive input regressors.

reg = linearRegressor({'y1','u1'},{1:4,0:4});

Estimate the nonlinear ARX model.

sys = nlarx(z7,reg,w);

Examine the postestimation properties of the output function.

disp(sys.OutputFcn)
Wavelet Network
Inputs: y1(t-1), y1(t-2), y1(t-3), y1(t-4), u1(t), u1(t-1), u1(t-2), u1(t-3), u1(t-4)
Output: y1(t)

Nonlinear Function: Wavelet network with 5 units
Linear Function: initialized to [-1.12 0.469 1.25 0.556 -0.81 -0.261 -0.074 0.711 1.15]
Output Offset: fixed to 1

Inputs: {1x9 cell}
Outputs: {'y1(t)'}
NonlinearFcn: '<Wavelet and scaling function units and their parameters>'
LinearFcn: '<Linear function parameters>'
Offset: '<Offset parameters>'
EstimationOptions: '<Estimation options>'
disp(sys.OutputFcn.Input)
Function inputs

Name: {1x9 cell}
Mean: [5.3829e-17 7.1772e-17 2.6914e-17 0 -4.4857e-18 -4.4857e-17 ... ]
Range: [2x9 double]
disp(sys.OutputFcn.NonlinearFcn)
Wavelet and scaling function units and their parameters

NumberOfUnits: 5
Parameters: '<Wavelet parameters>'
Free: 1
Inputs: {1x9 cell}
Outputs: {'y1(t):Nonlinear'}

Specify idWaveletNetwork for the model output nonlinearity and set the arguments for NumUnits to 5 and UseLinearFcn and UseOffset to false.

w = idWaveletNetwork(5,false,false);

An alternative method for removing the linear function and offset is to use dot notation after creating the nonlinearity.

w.LinearFcn.Use = false;
w.Offset.Use = false;

Estimate the model, using an order specification of [4 4 1].

sys = nlhw(ThrottleData,[4 4 1],[],w);

Compare the simulated model response with the estimation data.

compare(ThrottleData,sys);

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## Algorithms

You can use idWaveletNetwork in both nonlinear ARX and Hammerstein-Wiener models. The algorithms for estimating idWaveletNetwork parameters depend on which model you are estimating.

• In a nonlinear ARX model, idWaveletNetwork uses either a noniterative or an iterative technique for predicting the parameters, depending on option settings in nlarxOptions.

• If the Focus option is set to 'prediction', then idWaveletNetwork uses a fast noniterative technique to estimate parameters [1]. Successive refinements after the first estimation use an iterative algorithm.

• If the Focus option is set to 'simulation', then idWaveletNetwork uses an iterative technique to estimate parameters.

• To always use either an iterative or a noniterative algorithm, specify the IterativeWavenet property of nlarxOptions as 'on' or 'off', respectively.

• In a Hammerstein-Wiener model, idWaveletNetwork uses iterative minimization to determine the parameters.

## References

[1] Qinghua Zhang. “Using Wavelet Network in Nonparametric Estimation.” IEEE Transactions on Neural Networks 8, no. 2 (March 1997): 227–36. https://doi.org/10.1109/72.557660.

## Version History

Introduced in R2007a

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Behavior changed in R2022a

Not recommended starting in R2021b

Not recommended starting in R2021a