System Identification Toolbox™ software provides several scalar nonlinearity estimators, for Hammerstein-Wiener models. The nonlinearity estimators are available for both the input and output nonlinearities f and h, respectively. For more information about f and h, see Structure of Hammerstein-Wiener Models.
Each nonlinearity estimator corresponds to an object class in this toolbox. When you estimate Hammerstein-Wiener models in the System Identification app, the toolbox creates and configures objects based on these classes. You can also create and configure nonlinearity estimators at the command line. For a detailed description of each estimator, see the references page of the corresponding nonlinearity class.
|A piecewise linear function parameterized by breakpoint locations.||By default, the number of breakpoints is 10.|
|One layer sigmoid network||
is the sigmoid function . is a row vector such that is a scalar.
|Default number of units n is 10.|
where is the wavelet function.
|By default, the estimation algorithm determines the number of units n automatically.|
|Saturation||Parameterize hard limits on the signal value as upper and lower saturation limits.||Use to model known saturation effects on signal amplitudes.|
|Dead zone||Parameterize dead zones in signals as the duration of zero response.||Use to model known dead zones in signal amplitudes.|
|Single-variable polynomial of a degree that you specify.||By default, the polynomial degree is 1.|
Excludes the input or output nonlinearity from the model structure to achieve a Wiener or Hammerstein configuration, respectively.
Excluding both the input and output nonlinearities reduces the Hammerstein-Wiener structure to a linear transfer function.
|Useful for configuring multi-input, multi-output (MIMO) models to exclude nonlinearities from specific input and output channels.|
Similar to sigmoid network but you specify .
(For advanced use)
Uses the unit function that you specify.