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nonlinear model identification -凯发k8网页登录

identify nonlinear arx, hammerstein-wiener, and grey-box models

use nonlinear model identification when a linear model does not completely capture your system dynamics. you can identify nonlinear models in the system identification app or at the command line. system identification toolbox™ enables creation and estimation of three nonlinear model structures:

  • nonlinear arx models — represent nonlinearities in your system using dynamic nonlinear mapping objects such as wavelet networks, tree-partitioning, and sigmoid networks.

  • hammerstein-wiener models — estimate static nonlinearities in an otherwise linear system.

  • nonlinear grey-box models — represent your nonlinear system using ordinary differential or difference equations (odes) with unknown parameters.

  • neural state-space models — use neural networks to represent the functions that define the nonlinear state space realization of your system.

nonlinear model identification requires uniformly sampled time-domain data. your data can have one or more input and output channels. you can also model time series data using nonlinear arx and nonlinear grey-box models. for more information, see .

you can use the identified models to simulate and predict model output at the command line, in the app, or in simulink®. if you have control system toolbox™, you can also linearize your model and use it for control-system design. for more information, see .

categories


  • identified nonlinear models, black-box modeling, and regularization
  • nonlinear arx models
    nonlinear behavior modeled using dynamic networks such as sigmoid and wavelet
  • hammerstein-wiener models
    connection of linear dynamic systems with static nonlinearities such as saturation and dead zone

  • estimate coefficients of nonlinear differential, difference and state-space equations
  • neural state-space models
    use neural networks to represent the functions defining the nonlinear state space realization of your system

  • reduce computational complexity of models by creating accurate surrogates
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