Main Content

Nonlinear Model Identification Basics

Identified nonlinear models, black-box modeling, and regularization

Examples and How To

Identify Nonlinear Black-Box Models Using System Identification App

Identify nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app.


Types of Model Objects

Model object types include numeric models, for representing systems with fixed coefficients, and generalized models for systems with tunable or uncertain coefficients.

About Identified Nonlinear Models

Dynamic models in System Identification Toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system.

Nonlinear Model Structures

Construct model objects for nonlinear model structures, access model properties.

Available Nonlinear Models

The System Identification Toolbox software provides three types of nonlinear model structures:

Black-Box Modeling

Black-box modeling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model.

Modeling Multiple-Output Systems

Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.

Preparing Data for Nonlinear Identification

Estimating nonlinear ARX and Hammerstein-Wiener models requires uniformly sampled time-domain data.

Loss Function and Model Quality Metrics

Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models.

Regularized Estimates of Model Parameters

Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.

Estimation Report

The estimation report contains information about the results and options used for a model estimation.

Next Steps After Getting an Accurate Model

How you can work with identified models.