Nonlinear Model Identification Basics
Examples and How To
Identify 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.
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.
Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.
Estimating nonlinear ARX and Hammerstein-Wiener models requires uniformly sampled time-domain data.
Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models.
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.
How you can work with identified models.