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. The model is dynamic because the output value at the current time depends on the input-output values at previous time instants. Therefore, dynamic models have memory of the past. You can use the input-output relationships to compute the current output from previous inputs and outputs. Dynamic models have states, where a state vector contains the information of the past.
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. Your data can have one or more input and output channels.
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. This report is stored in the
property of the estimated model. The exact contents of the report depend on the estimator
function you use to obtain the model.
How you can work with identified models.