Simulink 深度学习
使用 Simulink 扩展深度学习工作流
通过使用 Deep Learning Toolbox™ 中包含的 Deep Neural Networks 模块库中的模块,或使用 Computer Vision Toolbox™ 中包含的 Analysis & Enhancement 模块库中的 Deep Learning Object Detector 模块,在 Simulink® 模型中实现深度学习功能。
Simulink 中的深度学习功能使用需要支持的编译器的 MATLAB Function 模块。对于大多数平台,会随 MATLAB® 安装提供一个默认的 C 编译器。使用 C++ 语言时,必须安装兼容的 C++ 编译器。要查看支持的编译器列表,请打开支持和兼容的编译器,点击与您的操作系统对应的选项卡,找到 Simulink Product Family 表,并转至 For Model Referencing, Accelerator mode, Rapid Accelerator mode, and MATLAB Function blocks 列。如果您的系统上安装了多个 MATLAB 支持的编译器,可以使用 mex -setup
命令更改默认编译器。请参阅更改默认编译器。
模块
Image Classifier | 使用经过训练的深度学习神经网络对数据进行分类 (自 R2020b 起) |
Predict | 使用经过训练的深度学习神经网络预测响应 (自 R2020b 起) |
Stateful Classify | Classify data using a trained deep learning recurrent neural network (自 R2021a 起) |
Stateful Predict | Predict responses using a trained recurrent neural network (自 R2021a 起) |
Deep Learning Object Detector | Detect objects using trained deep learning object detector (自 R2021b 起) |
TensorFlow Model Predict | Predict responses using pretrained Python TensorFlow model (自 R2024a 起) |
PyTorch Model Predict | Predict responses using pretrained Python PyTorch model (自 R2024a 起) |
ONNX Model Predict | Predict responses using pretrained Python ONNX model (自 R2024a 起) |
Custom Python Model Predict | Predict responses using pretrained custom Python model (自 R2024a 起) |
主题
图像
- Classify Images in Simulink Using GoogLeNet
This example shows how to classify an image in Simulink® using theImage Classifier
block. - Acceleration for Simulink Deep Learning Models
Improve simulation speed with accelerator and rapid accelerator modes. - Lane and Vehicle Detection in Simulink Using Deep Learning
This example shows how to use deep convolutional neural networks inside a Simulink® model to perform lane and vehicle detection. - Classify ECG Signals in Simulink Using Deep Learning
This example shows how to use wavelet transforms and a deep learning network within a Simulink (R) model to classify ECG signals. - Classify Images in Simulink with Imported TensorFlow Network
Import a pretrained TensorFlow™ network usingimportTensorFlowNetwork
, and then use the Predict block for image classification in Simulink. - Classify Images Using TensorFlow Model Predict Block
Classify images using TensorFlow Model Predict block. - Classify Images Using ONNX Model Predict Block
Classify images using ONNX Model Predict block. - Classify Images Using PyTorch Model Predict Block
Classify images using PyTorch Model Predict block.
序列
- Predict and Update Network State in Simulink
This example shows how to predict responses for a trained recurrent neural network in Simulink® by using theStateful Predict
block. - Classify and Update Network State in Simulink
This example shows how to classify data for a trained recurrent neural network in Simulink® by using theStateful Classify
block. - Speech Command Recognition in Simulink
Detect the presence of speech commands in audio using a Simulink model. - Time Series Prediction in Simulink Using Deep Learning Network
This example shows how to use an LSTM deep learning network inside a Simulink® model to predict the remaining useful life (RUL) of an engine. - Battery State of Charge Workflow
An example workflow for training, compressing, and using a deep learning network in Simulink. - Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) to replace a Simscape component in a Simulink® model by training a long short-term memory (LSTM) neural network. - Improve Performance of Deep Learning Simulations in Simulink
This example shows how to use code generation to improve the performance of deep learning simulations in Simulink®.
增强学习
- Create Simulink Environment and Train Agent
Train a controller using reinforcement learning with a plant modeled in Simulink as the training environment. - Train DDPG Agent for Adaptive Cruise Control
Train a reinforcement learning agent for an adaptive cruise control application. - Train DQN Agent for Lane Keeping Assist Using Parallel Computing
Train a reinforcement learning agent for a lane keeping assist application. - Train DDPG Agent for Path-Following Control
Train a reinforcement learning agent for a lane following application.
代码生成
- 从 Simulink 应用生成深度学习代码
生成用于在桌面或嵌入式目标上部署的 C/C++ 和 GPU 代码 - Export Network to FMU
This example shows how to export a trained network as a Functional Mock-up Unit (FMU). (自 R2023b 起)