Nonlinear ARX Models - MATLAB & Simulink - MathWorks Deutschland (2025)

Use nonlinear ARX models to represent nonlinearities in your system using dynamic nonlinearity estimators such as wavelet networks, tree-partitioning, and sigmoid networks. In the toolbox, these models are represented as idnlarx objects. You can estimate Nonlinear ARX models in the System Identification app, or at the command line using the nlarx command.

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• What are Nonlinear ARX Models?

  Understand the structure of a nonlinear ARX model.

• Available Mapping Functions for Nonlinear ARX Models

  Choose from sigmoid, wavelet, tree partition, linear,neural, and custom network nonlinearities.

• Identifying Nonlinear ARX Models
  

  Specify the Nonlinear ARX structure, and configurethe estimation algorithm.

  • Identifizieren nichtlinearer Black-Box-Modelle mithilfe der App „System Identification“
  • Estimate Nonlinear ARX Models at the Command Line
  • Estimate Nonlinear ARX Models Initialized Using Linear ARX Models
• Validate Nonlinear ARX Models

  Plot model nonlinearities, analyze residuals, andsimulate and predict model output.

• Using Nonlinear ARX Models

  Simulate, predict, and forecast model output, linearizenonlinear ARX 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 Nonlinear ARX Model Output

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