Identify Nonlinear Black-Box Models Using System Identification App
Identifying nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app.
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.
Construct model objects for nonlinear model structures, access model properties.
The System Identification Toolbox software provides three types of nonlinear model structures:
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
Supported models for multiple-output systems.
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.
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.