Linear Model Identification Basics
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
- Identification de modèles linéaires à l’aide de l’application System Identification
Identification de modèles boîte noire linéaires à partir de données à entrée unique/sortie unique (SISO) avec l’application System Identification.
- Identification de modèles linéaires avec l'interface en lignes de commande
Identification de modèles linéaires à partir de données à entrées multiples/sortie unique (MISO) en utilisant les commandes de System Identification Toolbox.
- Frequency Domain Identification: Estimating Models Using Frequency Domain Data
This example shows how to estimate models using frequency domain data.
- Estimation Report
The estimation report contains information about the results and options used for a model estimation.
Select Model Structure
- About Identified Linear Models
System Identification Toolbox software uses objects to represent a variety of linear and nonlinear model structures.
- Available Linear Models
Summary of linear model types that you can use for system identification.
- Black-Box Modeling
Black-box modeling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model.
- Model Structure Selection: Determining Model Order and Input Delay
This example shows some methods for choosing and configuring the model structure.
- Modeling Multiple-Output Systems
Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.
- Types of Model Objects
Model object types include numeric models, for representing systems with fixed coefficients, and generalized models for systems with tunable or uncertain coefficients.
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
Structureproperty of the mode object.
- Regularized Identification of Dynamic Systems
This example shows the benefits of regularization for identification of linear and nonlinear models.
- Estimate Regularized ARX Model Using System Identification App
This example shows how to estimate regularized ARX models using automatically generated regularization constants in the System Identification app.
- Regularized Estimates of Model Parameters
Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.
- Loss Function and Model Quality Metrics
Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models.
- Effect of Input Intersample Behavior on Continuous-Time Models
The intersample behavior of the input signals influences the estimation, simulation and prediction of continuous-time models.