Conceptos básicos de la identificación de modelos no lineales
R2026aUtilice la identificación de modelos no lineales cuando un modelo lineal no capture por completo la dinámica del sistema. Puede identificar modelos no lineales en la app System Identification o en la línea de comandos. System Identification Toolbox™ permite la creación y estimación de cuatro estructuras de modelos no lineales:
Modelos ARX no lineales: represente no linealidades en el sistema utilizando objetos de mapeo no lineales dinámicos como redes wavelet, partición en árbol y redes sigmoides.
Modelos Hammerstein-Wiener: realice la estimación de no linealidades estáticas en un sistema por lo demás lineal.
Modelos de caja gris no lineales: represente el sistema no lineal utilizando ecuaciones diferenciales o de diferencias ordinarias (EDO) con parámetros desconocidos.
Modelos en espacio de estados neuronales: utilice redes neuronales para representar las funciones que definen la realización en espacio de estados no lineales del sistema.
Temas
Modelos identificados no lineales
- 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. - 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.
Estimación de modelos
- Identify Nonlinear Black-Box Models Using System Identification App
Identify nonlinear black-box models from single-input/single-output (SISO) data using the System Identification app. - 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.
