nlhw

Estimate Hammerstein-Wiener model

Syntax

sys = nlhw(Data,Orders)

sys = nlhw(Data,Orders,InputNL,OutputNL)

sys = nlhw(Data,LinModel)

sys = nlhw(Data,LinModel,InputNL,OutputNL)

sys = nlhw(Data,sys0)

sys = nlhw(___,Options)

Description

sys = nlhw(Data,Orders) creates and estimates a Hammerstein-Wiener model using the estimation data, model orders and delays, and default piecewise linear functions as input and output nonlinearity estimators.

sys = nlhw(Data,Orders,InputNL,OutputNL) specifies InputNL and OutputNL as the input and output nonlinearity estimators, respectively.

sys = nlhw(Data,LinModel) uses a linear model to specify the model orders and delays, and default piecewise linear functions for the input and output nonlinearity estimators.

sys = nlhw(Data,LinModel,InputNL,OutputNL) specifies InputNL and OutputNL as the input and output nonlinearity estimators, respectively.

sys = nlhw(Data,sys0) refines or estimates the parameters of a Hammerstein-Wiener model, sys0, using the estimation data.

Use this syntax to:

• Update the parameters of a previously estimated model to improve the fit to the estimation data. In this case, the estimation algorithm uses the parameters of sys0 as initial guesses.

• Estimate the parameters of a model previously created using the idnlhw constructor. Prior to estimation, you can configure the model properties using dot notation.

sys = nlhw(___,Options) specifies additional model estimation options. Use Options with any of the previous syntaxes.

Examples

Estimate a Hammerstein-Wiener Model

load iddata3
m1 = nlhw(z3,[4 2 1]);

Estimate a Hammerstein Model with Saturation

Load data.

load twotankdata;
z = iddata(y,u,0.2,'Name','Two tank system');
z1 = z(1:1000);

Create a saturation object with lower limit of 0 and upper limit of 5.

InputNL = idSaturation('LinearInterval',[0 5]);

Estimate model with no output nonlinearity.

m = nlhw(z1,[2 3 0],InputNL,[]);

Estimate Hammerstein-Wiener Model with a Custom Network Nonlinearity

Generating a custom network nonlinearity requires the definition of a user-defined unit function.

Define the unit function and save it as gaussunit.m.

function [f,g,a] = gaussunit(x)
% Custom unit function nonlinearity.
%
% Copyright 2015 The MathWorks, Inc.
f = exp(-x.*x);
if nargout>1
  g = -2*x.*f;
  a = 0.2;
end

Create a custom network nonlinearity using the gaussunit function.

H = @gaussunit;
CNet = idCustomNetwork(H);

Load the estimation data.

load twotankdata;
z = iddata(y,u,0.2,'Name','Two tank system');
z1 = z(1:1000);

Estimate a Hammerstein-Wiener model using the custom network.

m = nlhw(z1,[5 1 3],CNet,[]);

Estimate Default Hammerstein-Wiener Model Using an Input-Output Polynomial Model of OE Structure

Estimate linear OE model.

load throttledata.mat
Tr = getTrend(ThrottleData); 
Tr.OutputOffset = 15;
DetrendedData = detrend(ThrottleData, Tr);
opt = oeOptions('Focus','simulation');
LinearModel = oe(DetrendedData,[1 2 1],opt);

Estimate Hammerstein-Wiener model using OE model as its linear component and saturation as its output nonlinearity.

sys = nlhw(ThrottleData,LinearModel,[],idSaturation);

Estimate a Hammerstein-Wiener Model Using idnlhw to first Define the Model Properties

Load the estimation data.

load iddata1

Construct a Hammerstein-Wiener model using idnlhw to define the model properties B and F.

sys0 = idnlhw([2,2,0],[],'idWaveletNetwork');
sys0.B{1} = [0.8,1];
sys0.F{1} = [1,-1.2,0.5];

Estimate the model.

sys = nlhw(z1,sys0);

Estimate a Hammerstein-Wiener model using nlhw to define the model properties B and F.

sys2 = nlhw(z1,[2,2,0],[],'idWaveletNetwork','B',{[0.8,1]},'F',{[1,-1.2,0.5]});

Compare the two estimated models to see that they are equivalent.

compare(z1,sys,'g',sys2,'r--');

Refine a Hammerstein-Wiener Model Using Successive Calls of nlhw

Estimate a Hammerstein-Wiener Model.

load iddata3
sys = nlhw(z3,[4 2 1],'idSigmoidNetwork','idWaveletNetwork');

Refine the model, sys.

sys = nlhw(z3,sys);

Estimate Hammerstein-Wiener Model Using an Estimation Option Set

Create estimation option set for nlhw to view estimation progress, use the Levenberg-Marquardt search method, and set the maximum iteration steps to 50.

opt = nlhwOptions;
opt.Display = 'on';
opt.SearchMethod = 'lm';    
opt.SearchOptions.MaxIterations = 50;

Load data and estimate the model.

load iddata3
sys = nlhw(z3,[4 2 1],idSigmoidNetwork,idPiecewiseLinear,opt);

Input Arguments

Data — Time domain data

iddata object

Time-domain estimation data, specified as an iddata. 

Orders — Order and delays of the linear subsystem transfer function

[nb nf nk] vector of positive integers | [nb nf nk] vector of matrices

Order and delays of the linear subsystem transfer function, specified as a [nb nf nk] vector.

Dimensions of Orders:

• For a SISO transfer function, Orders is a vector of positive integers.

  nb is the number of zeros plus 1, nf is the number of poles, and nk is the input delay.

• For a MIMO transfer function with nu inputs and ny outputs, Orders is a vector of matrices.

  nb, nf, and nk are ny-by-nu matrices whose i-jth entry specifies the orders and delay of the transfer function from the jth input to the ith output.

InputNL — Input static nonlinearity

'idPiecewiseLinear' (default) | 'idSigmoidNetwork' | 'idWaveletNetwork' | 'idSaturation' | 'idDeadZone' | 'idPolynomial1D' | 'idUnitGain' | nonlinearity estimator object | array of nonlinearity estimators

Input static nonlinearity estimator, specified as one of the following.

'idPiecewiseLinear' or idPiecewiseLinear object (default)	Piecewise linear function
'idSigmoidNetwork' or idSigmoidNetwork object	Sigmoid network
'idWaveletNetwork' or idWaveletNetwork object	Wavelet network
'idSaturation' or idSaturation object	Saturation
'idDeadZone' or idDeadZone object	Dead zone
'idPolynomial1D' or idPolynomial1D object	One-dimensional polynomial
'idUnitGain' or [] oridUnitGain object	Unit gain
idCustomNetwork object	Custom network — Similar to idSigmoidNetwork, but with a user-defined replacement for the sigmoid function.

Specifying a character vector, for example 'idSigmoidNetwork', creates a nonlinearity estimator object with default settings. Use object representation instead to configure the properties of a nonlinearity estimator.

InputNL = idWaveletNetwork;
InputNL.NumberOfUnits = 10;

Alternatively, use the associated input nonlinearity estimator function with Name-Value pair arguments.

InputNL = idWaveletNetwork('NumberOfUnits',10);

For nu input channels, you can specify nonlinear estimators individually for each input channel by setting InputNL to an nu-by-1 array of nonlinearity estimators.

InputNL = [idSigmoidNetwork('NumberofUnits',5); idDeadZone([-1,2])]

To specify the same nonlinearity for all inputs, specify a single input nonlinearity estimator.

OutputNL — Output static nonlinearity

'idPiecewiseLinear' (default) | 'idSigmoidNetwork' | 'idWaveletNetwork' | 'idSaturation' | 'idDeadZone' | 'idPolynomial1D' | 'idUnitGain' | nonlinearity estimator object | array of nonlinearity estimators

Output static nonlinearity estimator, specified as one of the following:

'idPiecewiseLinear' or idPiecewiseLinear object (default)	Piecewise linear function
'idSigmoidNetwork' or idSigmoidNetwork object	Sigmoid network
'idWaveletNetwork' or idWaveletNetwork object	Wavelet network
'idSaturation' or idSaturation object	Saturation
'idDeadZone' or idDeadZone object	Dead zone
'idPolynomial1D' or idPolynomial1D object	One-dimensional polynomial
'idUnitGain' or [] oridUnitGain object	Unit gain
idCustomNetwork object	Custom network — Similar to idSigmoidNetwork, but with a user-defined replacement for the sigmoid function.

Specifying a character vector creates a nonlinearity estimator object with default settings. Use object representation instead to configure the properties of a nonlinearity estimator.

OutputNL = idSigmoidNetwork;
OutputNL.NumberOfUnits = 10;

Alternatively, use the associated input nonlinearity estimator function with Name-Value pair arguments.

OutputNL = idSigmoidNetwork('NumberOfUnits',10);

For ny output channels, you can specify nonlinear estimators individually for each output channel by setting OutputNL to an ny-by-1 array of nonlinearity estimators. To specify the same nonlinearity for all outputs, specify a single output nonlinearity estimator.

LinModel — Discrete time linear model

idpoly | idss with K = 0 | idtf

Discrete-time linear model used to specify the linear subsystem, specified as one of the following:

• Input-output polynomial model of Output-Error (OE) structure (idpoly)

• State-space model with no disturbance component (idss with K = 0)

• Transfer function model (idtf)

Typically, you estimate the model using oe, n4sid, or tfest.

sys0 — Hammerstein-Wiener model

idnlhw object

Hammerstein-Wiener model, specified as an idnlhw object. sys0 can be:

• A model previously created using idnlhw to specify model properties.

• A model previously estimated using nlhw, that you want to update using a new estimation data set.

  You can also refine sys0 using the original estimation data set. If the previous estimation stopped when the numerical search was stuck at a local minima of the cost function, use init to first randomize the parameters of sys0. See sys0.Report.Termination for search stopping conditions. Using init does not guarantee a better solution on further refinement.

Options — Estimation options

nlhwOptions option set

Estimation options for Hammerstein-Wiener model identification, specified as an nlhwOptions option set.

Output Arguments

sys — Estimated Hammerstein-Wiener model

idnlhw object

Estimated Hammerstein-Wiener model, returned as an idnlhw object. The model is estimated using the specified model orders and delays, input and output nonlinearity estimators, and estimation options.

Information about the estimation results and options used is stored in the Report property of the model. Report has the following fields:

Report Field	Description
Status	Summary of the model status, which indicates whether the model was created by construction or obtained by estimation.
Method	Estimation command used.
Fit	Quantitative assessment of the estimation, returned as a structure. See Loss Function and Model Quality Metrics for more information on these quality metrics. The structure has the following fields: Field Description FitPercent Normalized root mean squared error (NRMSE) measure of how well the response of the model fits the estimation data, expressed as the percentage fitpercent = 100(1-NRMSE). LossFcn Value of the loss function when the estimation completes. MSE Mean squared error (MSE) measure of how well the response of the model fits the estimation data. FPE Final prediction error for the model. AIC Raw Akaike Information Criteria (AIC) measure of model quality. AICc Small-sample-size corrected AIC. nAIC Normalized AIC. BIC Bayesian Information Criteria (BIC).
Parameters	Estimated values of model parameters.
OptionsUsed	Option set used for estimation. If no custom options were configured, this is a set of default options. See nlhwOptions for more information.
RandState	State of the random number stream at the start of estimation. Empty, [], if randomization was not used during estimation. For more information, see rng.
DataUsed	Attributes of the data used for estimation, returned as a structure with the following fields. Field Description Name Name of the data set. Type Data type. Length Number of data samples. Ts Sample time. InterSample Input intersample behavior, returned as one of the following values: 'zoh' — Zero-order hold maintains a piecewise-constant input signal between samples. 'foh' — First-order hold maintains a piecewise-linear input signal between samples. 'bl' — Band-limited behavior specifies that the continuous-time input signal has zero power above the Nyquist frequency. InputOffset Offset removed from time-domain input data during estimation. For nonlinear models, it is []. OutputOffset Offset removed from time-domain output data during estimation. For nonlinear models, it is [].
Termination	Termination conditions for the iterative search used for prediction error minimization, returned as a structure with the following fields: Field Description WhyStop Reason for terminating the numerical search. Iterations Number of search iterations performed by the estimation algorithm. FirstOrderOptimality $$\infty$$-norm of the gradient search vector when the search algorithm terminates. FcnCount Number of times the objective function was called. UpdateNorm Norm of the gradient search vector in the last iteration. Omitted when the search method is 'lsqnonlin' or 'fmincon'. LastImprovement Criterion improvement in the last iteration, expressed as a percentage. Omitted when the search method is 'lsqnonlin' or 'fmincon'. Algorithm Algorithm used by 'lsqnonlin' or 'fmincon' search method. Omitted when other search methods are used. For estimation methods that do not require numerical search optimization, the Termination field is omitted.

For more information, see Estimation Report.

Compatibility Considerations

Use of previous idnlarx and idnlhw mapping object names is not recommended.

Not recommended starting in R2021b

Starting in R2021b, the mapping objects (also known as nonlinearities) used in the nonlinear components of the idnlarx and idnlhw objects have been renamed. The following table lists the name changes.

Pre-R2021b Name	R2021b Name
wavenet	idWaveletNetwork
sigmoidnet	idSigmoidNetwork
treepartition	idTreePartition
customnet	idCustomNetwork
saturation	idSaturation
deadzone	idDeadZone
pwlinear	idPiecewiseLinear
poly1d	idPolynomial1D
unitgain	idUnitGain
linear	idLinear
neuralnet	idFeedforwardNetwork

Scripts with the old names still run normally, although they will produce a warning. Consider using the new names for continuing compatibility with newly developed features and algorithms. There are no plans to exclude the use of these object names at this time

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

Parallel computing support is available for estimation using the lsqnonlin search method (requires Optimization Toolbox™). To enable parallel computing, use nlhwOptions, set SearchMethod to 'lsqnonlin', and set SearchOptions.Advanced.UseParallel to true.

For example:

opt = nlhwOptions;
opt.SearchMethod = 'lsqnonlin';
opt.SearchOptions.Advanced.UseParallel = true;