Nonlinear Model Identification Basics

Identified nonlinear models, black-box modeling, and regularization

Rubriques

Identification de modèles boîte noire non linéaires avec l’application System Identification
Identification de modèles boîte noire non linéaires à partir de données à entrée unique/sortie unique (SISO) avec l’application System Identification.
About Identified Nonlinear Models
Dynamic models in System Identification Toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system.
Nonlinear Model Structures
Construct model objects for nonlinear model structures, access model properties.
Available Nonlinear Models
The System Identification Toolbox software provides four types of nonlinear model structures:
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.
Modeling Multiple-Output Systems
Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.
Preparing Data for Nonlinear Identification
Estimating nonlinear ARX and Hammerstein-Wiener models requires uniformly sampled time-domain data.
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.
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.
Estimation Report
The estimation report contains information about the results and options used for a model estimation.
Next Steps After Getting an Accurate Model
How you can work with identified models.
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.