A Hammerstein-Wiener plot displays the static input and output nonlinearities
and linear responses of a Hammerstein-Wiener model.

Examining a Hammerstein-Wiener plot can help you determine whether
you have selected a complicated nonlinearity for modeling your system.
For example, suppose you use a piecewise-linear input nonlinearity
to estimate your model, but the plot indicates saturation behavior.
You can estimate a new model using the simpler saturation nonlinearity
instead. For multivariable systems, you can use the Hammerstein-Wiener
plot to determine whether to exclude nonlinearities for specific channels.
If the nonlinearity for a specific input or output channel does not
exhibit strong nonlinear behavior, you can estimate a new model after
setting the nonlinearity at that channel to unit gain.

You can generate these plots in the **System Identification** app and at the command
line. In the plot window, you can view the nonlinearities and linear responses by
clicking one of the three blocks that represent the model:

*u*_{NL} (*input
nonlinearity*)— Click this block to view the static
nonlinearity at the input to the `Linear Block`

.
The plot displays `evaluate(M.InputNonlinearity,u)`

where `M`

is
the Hammerstein-Wiener model, and `u`

is the input
to the input nonlinearity block. For information about the blocks,
see Structure of Hammerstein-Wiener Models.

`Linear Block`

— Click this
block to view the Step, impulse, Bode, and pole-zero response plots
of the embedded linear model (`M.LinearModel`

). By
default, a step plot of the linear model is displayed.

*y*_{NL} (*output
nonlinearity*) — Click this block to view the static
nonlinearity at the output of the `Linear Block`

.
The plot displays `evaluate(M.OutputNonlinearity,x)`

,
where `x`

is the output of the linear block.

To learn more about how to configure the linear and nonlinear
blocks plots, see Configuring a Hammerstein-Wiener Plot.