• documentation
main content

hammerstein-凯发k8网页登录

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 objects. you can estimate hammerstein-wiener models in the system identification app, or at the command line using the  command.

apps

system identificationidentify models of dynamic systems from measured data

functions

hammerstein-wiener model
estimate hammerstein-wiener model
option set for nlhw
set or randomize initial parameter values
obtain model parameters and associated uncertainty data
modify values of model parameters
custom network function for nonlinear arx and hammerstein-wiener models
create a dead-zone nonlinearity estimator object
class representing single-variable polynomial nonlinear estimator for hammerstein-wiener models
create a piecewise-linear nonlinearity estimator object
create a saturation nonlinearity estimator object
sigmoid network function for nonlinear arx and hammerstein-wiener models
specify absence of nonlinearities for specific input or output channels in hammerstein-wiener models
wavelet network function for nonlinear arx and hammerstein-wiener models
gaussian process regression mapping function for nonlinear arx and hammerstein-wiener models (requires statistics and machine learning toolbox) (since r2021b)
multilayer neural network mapping function for nonlinear arx models and hammerstein-wiener models (requires statistics and machine learning toolbox or deep learning toolbox) (since r2023b)
evaluate output values of idnlarx or idnlhw mapping object array for given set of input values
simulate response of identified model
option set for sim
compare identified model output with measured output
option set for compare
plot input and output nonlinearity, and linear responses of hammerstein-wiener model (since r2023a)
evaluate output values of idnlarx or idnlhw mapping object array for given set of input values
compute operating point for hammerstein-wiener model
option set for findop
construct operating point specification object for idnlhw model
linearize hammerstein-wiener model
linear approximation of nonlinear arx and hammerstein-wiener models for given input

blocks

simulate hammerstein-wiener model in simulink software
export simulation data as iddata object to matlab workspace
import time-domain data stored in iddata object in matlab workspace

topics


  • understand the structure of hammerstein-wiener models.


  • choose from a set of scalar nonlinearity estimators that you can use for both input and output estimators in hammerstein-wiener models.


  • specify the hammerstein-wiener model structure, and configure the estimation algorithm.


  • plot model nonlinearities, analyze residuals, and simulate model output.


  • simulate and predict model output, linearize hammerstein-wiener models, and import estimated models into the simulink® software.


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


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

网站地图