Modelado de orden reducido
Utilice Deep Learning Toolbox™ para tareas de modelado de orden reducido (ROM).
El modelado de orden reducido (ROM) es una técnica que puede simplificar modelos y simulaciones complejos y de alta fidelidad reduciendo la complejidad computacional y preservando el comportamiento y la precisión del modelo. Por ejemplo, puede reemplazar subsistemas computacionalmente intensivos en un modelo de Simulink con una red neuronal entrenada que hace predicciones realistas.
Funciones
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (Desde R2024b) |
Bloques
| Predict | Predecir respuestas usando una red neuronal de deep learning entrenada |
| Stateful Predict | Predict responses using a trained recurrent neural network (Desde R2021a) |
Temas
- Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) that acts as a virtual sensor in a Simulink® model using a long short-term memory (LSTM) neural network.
- Reduced Order Modeling Using Continuous-Time Echo State Network
This example shows how to train a continuous-time echo state network (CTESN) model to solve Robertson's equation.
- Generate Deep Learning SI Engine Model (Powertrain Blockset)
Generate a deep learning SI engine model from measured transient engine data.
- Implement Unsupported Deep Learning Layer Blocks
This example shows how to implement layers using Simulink blocks or MATLAB code in a MATLAB Function block.
Información relacionada
Ejemplos destacados
Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model
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. The low-order model takes the current (charge or discharge) and state of charge (SOC) as inputs and predicts voltage and temperature of an electric vehicle (EV) battery module while the battery is being cooled by an edge-cooled plate with a coolant at a constant flow rate. You train the low-order model and deploy it in Simulink® to compare it against the high-fidelity model.
