Main Content

Linear Model Identification Basics

Essential information for identifying linear models, selecting suitable model structures, constructing and modifying model object structures, and using regularized estimation

Linear models are the simplest models you can identify using System Identification Toolbox™. Use linear model identification when a linear model is sufficient to completely capture your system dynamics. To identify linear models, you start with time-domain or frequency domain input-output data and a model structure, such as a state-space or transfer function model. The software iteratively adjusts the free model parameters in order to minimize the difference between the measured output and the simulated model response to the input data. The toolbox allows you to perform tasks such as the following:

  • Estimate linear models using a specific model structure.

  • Use a black-box modeling approach and explore which model structure best suits your data.

  • Construct a preliminary linear model and use it to initialize the parameters of the model you want to estimate.

  • Incorporate system knowledge into your model by fixing known parameters to specific values.

  • Use regularized estimation to reduce the uncertainty in your model by constraining model flexibility.


Identify Linear Models

Select Model Structure

Model Object Structures and Constraints

  • Linear Model Structures
    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.
  • Imposing Constraints on Model Parameter Values
    Constrain the adjustments that the estimation algorithm can make to individual model parameters by using the Structure property of the mode object.


Additional topics