Modellazione di ordine ridotto
Ridurre la complessità computazionale dei modelli creando surrogati accurati
La modellazione di ordine ridotto è una tecnica atta a ridurre la complessità computazionale o i requisiti di memorizzazione di un modello, preservando al contempo la fedeltà prevista entro un errore soddisfacente. Lavorare con un modello surrogato di ordine ridotto può semplificare l'analisi e la progettazione di controllo.
Argomenti
Nozioni di base sulla modellazione di ordine ridotto
- Reduced Order Modeling
Reduce computational complexity of models by creating accurate surrogates.
Metodi basati sui dati
- 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.
Metodi basati sulla linearizzazione
- 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.
