This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Nonlinear ARX Models

Nonlinear behavior modeled using dynamic networks such as sigmoid and wavelet


System Identification Identify models of dynamic systems from measured data


nlarx Estimate parameters of nonlinear ARX model
idnlarx Nonlinear ARX model
pem Prediction error estimate for linear and nonlinear model
isnlarx Detect nonlinearity in estimation data
customnet Custom nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
linear Class representing linear nonlinearity estimator for nonlinear ARX models
neuralnet Class representing neural network nonlinearity estimator for nonlinear ARX models
polyreg Powers and products of standard regressors
treepartition Class representing binary-tree nonlinearity estimator for nonlinear ARX models
wavenet Create a wavelet network nonlinearity estimator object
customreg Custom regressor for nonlinear ARX models
sigmoidnet Class representing sigmoid network nonlinearity estimator for nonlinear ARX and Hammerstein-Wiener models
addreg Add custom regressors to nonlinear ARX model
getreg Regressor expressions and numerical values in nonlinear ARX model
evaluate Value of nonlinearity estimator at given input
plot Plot nonlinearity of nonlinear ARX model
sim Simulate response of identified model
findop Compute operating point for Nonlinear ARX model
operspec Construct operating point specification object for idnlarx model
linearize Linearize nonlinear ARX model
linapp Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input
findstates Estimate initial states of model
init Set or randomize initial parameter values
getpvec Model parameters and associated uncertainty data
setpvec Modify value of model parameters
getDelayInfo Get input/output delay information for idnlarx model structure
nlarxOptions Option set for nlarx
findopOptions Option set for findop
findstatesOptions Option set for findstates
simOptions Option set for sim

Examples and How To

Estimate Nonlinear ARX Models in the App

Estimate nonlinear ARX models in the System Identification app.

Estimate Nonlinear ARX Models at the Command Line

Configure nonlinear ARX model estimation at the command line.

Estimate Nonlinear ARX Models Using Linear ARX Models

This example shows how to estimate nonlinear ARX models by using linear ARX models.

Low-Level Simulation and Prediction of Sigmoid Network

This example shows how the software computes the simulated and predicted output of the model as a result of evaluating the output of its nonlinearity estimator for given regressor values.


Structure of Nonlinear ARX Models

Understand the structure of a nonlinear ARX model.

Nonlinear ARX Model Extends the Linear ARX Structure

A nonlinear ARX model can be understood as an extension of a linear model.

Using Linear Model for Nonlinear ARX Estimation

You can use an ARX structure polynomial model (idpoly with only A and B as active polynomials) for nonlinear ARX estimation.

Nonlinearity Estimators for Nonlinear ARX Models

System Identification Toolbox™ software provides several nonlinearity estimators F(x) for nonlinear ARX models.

Ways to Configure Nonlinear ARX Estimation

Configure the model structure, orders, and delays, and specify the estimation algorithm.

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.

Validating Nonlinear ARX Models

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

Using Nonlinear ARX Models

Simulate and predict 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.

Was this topic helpful?