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.
System Identification | Identify models of dynamic systems from measured data |
What are Nonlinear ARX Models?
Understand the structure of a nonlinear ARX model.
Available Nonlinearity Estimators 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 configure the estimation algorithm.
Plot model nonlinearities, analyze residuals, and simulate and predict model output.
Simulate, predict, and forecast model output, linearize nonlinear 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 nonlinearity estimators and uses this output to compute the model response.