Nonlinear Model Identification Basics
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.