降阶建模
将 Deep Learning Toolbox™ 用于降阶建模任务。
降阶建模是一种在可接受误差范围内保持模型保真度的同时，降低模型计算复杂度或存储需求的技术。采用降阶模型可以简化控制设计和分析。例如，您可以用经过训练的神经网络替换 Simulink® 模型中的计算密集型子系统，以实现逼真的预测。
您可以创建在 Simulink 中建模的子系统的降阶模型 (ROM)，包括全阶高保真度第三方仿真模型。也可以利用现有的时域数据来创建 ROM。
降阶建模器提供了用于创建 ROM 的 UI 工作流。如需使用该 App，请按照获取和管理附加功能中的说明安装 Reduced Order Modeler for MATLAB® 支持包。
App
| 降阶建模器 | Create reduced order models based on Simulink models, subsystems within models, or simulation data (自 R2025b 起) |
函数
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (自 R2024b 起) |
模块
| Predict | 使用经过训练的深度学习神经网络预测响应 |
| Stateful Predict | 使用经过训练的循环神经网络预测响应 |
主题
- Reduced Order Model of a Jet Engine Turbine Blade (System Identification Toolbox)
Create a ROM of a jet engine turbine blade, using the long short-term memory (LSTM) and NSS model types.
- 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
Learn how to implement unsupported deep learning layer blocks.
相关信息
- 降阶建模 (Simulink)
- 降阶建模 (System Identification Toolbox)
- 使用 MATLAB 和 Simulink 进行降阶建模
精选示例
Reduced Order Model of a Jet Engine Turbine Blade
Create a ROM of a jet engine turbine blade, using the long short-term memory (LSTM) and NSS model types.
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.
