Modelado de orden reducido
Reduzca la complejidad computacional de modelos creando modelos sustitutos precisos
El modelado de orden reducido es una técnica para reducir la complejidad computacional o los requisitos de almacenamiento de un modelo al tiempo que se mantiene la fidelidad esperada dentro de un error satisfactorio. Trabajar con un modelo sustituto de orden reducido puede simplificar el análisis y el diseño de control.
Temas
Conceptos básicos del modelado de orden reducido
- Reduced Order Modeling
Reduce computational complexity of models by creating accurate surrogates.
Métodos basados en datos
- Nonlinear ARX Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a nonlinear ARX model. - Hammerstein-Wiener Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a Hammerstein-Wiener model. - Neural State-Space Model of SI Engine Torque Dynamics
This example describes reduced order modeling (ROM) of the nonlinear torque dynamics of a spark-ignition (SI) engine using a neural state-space model. - Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model
This example shows a reduced order modeling (ROM) workflow, where you use deep learning to obtain a low-order nonlinear state-space model that serves as a surrogate for a high-fidelity battery model.
Métodos basados en linealización
- Specify Linearization for Model Components Using System Identification (Simulink Control Design)
You can use System Identification Toolbox™ software to identify a linear system for a model component that does not linearize well, and use the identified system to specify its linearization. - Reduced Order Modeling of a Nonlinear Dynamical System as an Identified Linear Parameter Varying Model
Identify a linear parameter varying reduced order model of a cascade of nonlinear mass-spring-damper systems.
