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Hammerstein-Wiener Models

Connection of linear dynamic systems with static nonlinearities such as saturation and dead zone

Use Hammerstein-Wiener models to estimate static nonlinearities in an otherwise linear system. In the toolbox, these models are represented as idnlhw objects. You can estimate Hammerstein-Wiener models in the System Identification app, or at the command line using the nlhw command.


System Identification Identify models of dynamic systems from measured data


idnlhw Hammerstein-Wiener model
nlhw Estimate Hammerstein-Wiener model
nlhwOptions Option set for nlhw
init Set or randomize initial parameter values
getpvec Model parameters and associated uncertainty data
setpvec Modify value of model parameters
customnet Custom nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
deadzone Create a dead-zone nonlinearity estimator object
poly1d Class representing single-variable polynomial nonlinear estimator for Hammerstein-Wiener models
pwlinear Create a piecewise-linear nonlinearity estimator object
saturation Create a saturation nonlinearity estimator object
sigmoidnet Class representing sigmoid network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
unitgain Specify absence of nonlinearities for specific input or output channels in Hammerstein-Wiener models
wavenet Create a wavelet network nonlinearity estimator object
evaluate Value of nonlinearity estimator at given input
sim Simulate response of identified model
simOptions Option set for sim
compare Compare model output and measured output
compareOptions Option set for compare
plot Plot input and output nonlinearity, and linear responses of Hammerstein-Wiener model
evaluate Value of nonlinearity estimator at given input
findop Compute operating point for Hammerstein-Wiener model
findopOptions Option set for findop
operspec Construct operating point specification object for idnlhw model
linearize Linearize Hammerstein-Wiener model
linapp Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input


IDNLHW Model Simulate Hammerstein-Wiener model in Simulink software
IDDATA Sink Export iddata object to MATLAB workspace
IDDATA Source Import iddata object from MATLAB workspace


What are Hammerstein-Wiener Models?

Understand the structure of Hammerstein-Wiener models.

Available Nonlinearity Estimators for Hammerstein-Wiener Models

Choose from piecewise linear, sigmoid, wavelet, saturation, dead zone, polynomial, and custom network nonlinearities.

Identifying Hammerstein-Wiener Models

Specify the Hammerstein-Wiener model structure, and configure the estimation algorithm.

Validating Hammerstein-Wiener Models

Plot model nonlinearities, analyze residuals, and simulate model output.

Using Hammerstein-Wiener Models

Simulate and predict model output, linearize Hammerstein-Wiener models, and import estimated models into the Simulink® software.

Linear Approximation of Nonlinear Black-Box Models

Choose the approach for computing linear approximations, compute operating points for linearization, and linearize your model.

How the Software Computes Hammerstein-Wiener Model Output

How the software evaluates the output of nonlinearity estimators and uses this output to compute the model response.

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