Identify linear black-box models from single-input/single-output (SISO) data using the System Identification app.
Identify linear models from multiple-input/single-output (MISO) data using System Identification Toolbox™ commands.
Specify the values and constraints for the numerator, denominator and transport delays.
Specify how initial conditions are handled during model estimation in the app and at the command line.
This example shows how to estimate regularized ARX models using automatically generated regularization constants in 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.
System Identification Toolbox software uses objects to represent a variety of linear and nonlinear model structures.
Summary of linear model types that you can use for system identification.
Linear models in System Identification Toolbox take the form of model objects that are linear model structures. You can construct model objects directly or use estimation commands to both construct and estimate models. You can also modify the properties of existing model objects.
Black-box modeling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model.
Recommended model estimation sequence, from the simplest to the more complex model structures.
Constrain the adjustments that the estimation algorithm can make to individual model
parameters by using the
Structure property of the mode object.
Estimation requires you to specify the model order and delay. Many times, these values are not known.
The intersample behavior of the input signals influences the estimation, simulation and prediction of continuous-time models. A sampled signal is characterized only by its values at the sampling instants. However, when you apply a continuous-time input to a continuous-time system, the output values at the sampling instants depend on the inputs at the sampling instants and on the inputs between these points.
Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.
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.