Identifying linear black-box models from single-input/single-output (SISO) data using the System Identification app.
Identifying 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 some methods for choosing and configuring the model structure.
This example shows how to estimate models using frequency domain data.
This example shows the benefits of regularization for identification of linear and nonlinear models.
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.
A linear model is often sufficient to accurately describe the system dynamics and, in most cases, you should first try to fit linear models.
Linear 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.
Recommended model estimation sequence, from the simplest to the more complex model structures.
All identified linear (IDLTI) models, except
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.
Supported models for multiple-output systems.
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.