降阶建模
将 Deep Learning Toolbox™ 用于降阶建模 (ROM) 任务。
降阶建模 (ROM) 是一种技术，它可以通过降低计算复杂度来简化复杂的高保真模型和仿真，同时保持模型的行为和准确度。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。
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 | Predict responses using a trained recurrent neural network (自 R2021a 起) |
主题
- 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.
精选示例
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.
