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、Python Neural Networks 和 Deep Learning Layers 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。

要生成使用 Deep Learning Layers 模块库来表示网络的 Simulink 模型，请使用 exportNetworkToSimulink 函数。

Simulink 中某些深度学习功能使用的 MATLAB Function 模块需要支持的编译器。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。

函数

exportNetworkToSimulink Generate Simulink model that contains deep learning layer blocks that correspond to deep learning layer objects (自 R2024b 起)

模块

全部展开

网络

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)

激活层

Clipped ReLU Layer	Clipped Rectified Linear Unit (ReLU) layer (自 R2024b 起)
GELU Layer	Gaussian error linear unit (GELU) layer (自 R2024b 起)
Leaky ReLU Layer	Leaky rectified linear unit (ReLU) layer (自 R2024b 起)
ReLU Layer	Rectified linear unit (ReLU) layer (自 R2024b 起)
Sigmoid Layer	Sigmoid layer (自 R2024b 起)
Softmax Layer	Softmax layer (自 R2024b 起)
Tanh Layer	Hyperbolic tangent (tanh) layer (自 R2024a 起)

组合层

Addition Layer	Addition layer (自 R2024b 起)
Concatenation Layer	Concatenation layer (自 R2024b 起)
Depth Concatenation Layer	Depth concatenation layer (自 R2024b 起)
Multiplication Layer	Multiplication layer (自 R2024b 起)

卷积和全连接层

Convolution 1D Layer	1-D convolutional layer (自 R2024b 起)
Convolution 2D Layer	2-D convolutional layer (自 R2024b 起)
Convolution 3D Layer	3-D convolutional layer (自 R2024b 起)
Fully Connected Layer	Fully connected layer (自 R2024b 起)

输入层归一化

Rescale-Symmetric 1D	1-D input layer with `rescale-symmetric` normalization (自 R2024b 起)
Rescale-Symmetric 2D	2-D input layer with `rescale-symmetric` normalization (自 R2024b 起)
Rescale-Symmetric 3D	3-D input layer with `rescale-symmetric` normalization (自 R2024b 起)
Rescale-Zero-One 1D	1-D input layer with `rescale-zero-one` normalization (自 R2024b 起)
Rescale-Zero-One 2D	2-D input layer with `rescale-zero-one` normalization (自 R2024b 起)
Rescale-Zero-One 3D	3-D input layer with `rescale-zero-one` normalization (自 R2024b 起)
Zerocenter 1D	1-D input layer with `zerocenter` normalization (自 R2024b 起)
Zerocenter 2D	2-D input layer with `zerocenter` normalization (自 R2024b 起)
Zerocenter 3D	3-D input layer with `zerocenter` normalization (自 R2024b 起)
Zscore 1D	1-D input layer with `zscore` normalization (自 R2024b 起)
Zscore 2D	2-D input layer with `zscore` normalization (自 R2024b 起)
Zscore 3D	3-D input layer with `zscore` normalization (自 R2024b 起)

归一化层

Batch Normalization Layer	Batch normalization layer (自 R2024b 起)
Layer Normalization Layer	Layer normalization layer (自 R2024b 起)

池化层

Average Pooling 1D Layer	1-D average pooling layer (自 R2024b 起)
Average Pooling 2D Layer	2-D average pooling layer (自 R2024b 起)
Average Pooling 3D Layer	3-D average pooling layer (自 R2024b 起)
Global Average Pooling 1D Layer	1-D global average pooling layer (自 R2024b 起)
Global Average Pooling 2D Layer	2-D global average pooling layer (自 R2024b 起)
Global Average Pooling 3D Layer	3-D global average pooling layer (自 R2024b 起)
Global Max Pooling 1D Layer	1-D global max pooling layer (自 R2024b 起)
Global Max Pooling 2D Layer	2-D global max pooling layer (自 R2024b 起)
Global Max Pooling 3D Layer	3-D global max pooling layer (自 R2024b 起)
Max Pooling 1D Layer	1-D max pooling layer (自 R2024b 起)
Max Pooling 2D Layer	2-D max pooling layer (自 R2024b 起)
Max Pooling 3D Layer	3-D max pooling layer (自 R2024b 起)

序列层

Flatten Layer	Flatten layer (自 R2024b 起)
LSTM Layer	Long short-term memory (LSTM) layer for recurrent neural network (RNN) (自 R2024b 起)
LSTM Projected Layer	Long short-term memory (LSTM) projected layer for recurrent neural network (RNN) (自 R2024b 起)

实用工具层

Dropout Layer

Dropout layer (自 R2024b 起)

主题

深度学习层模块

List of Deep Learning Layer Blocks
Discover all the deep learning layer blocks in Simulink.
Implement Unsupported Deep Learning Layer Blocks
The exportNetworkToSimulink function converts deep learning layer objects in a dlnetwork object to deep learning layer blocks in a Simulink model.

图像

使用 GoogLeNet 对 Simulink 中的图像进行分类
此示例说明如何使用 Image Classifier 模块对 Simulink® 中的图像进行分类。此示例使用预训练的深度卷积神经网络 GoogLeNet 执行分类。
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 using importTensorFlowNetwork, and then use the Predict block for image classification in Simulink.

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 the Stateful 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 the Stateful 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.
Simulate Calorie Burn Using Neural Network in Simulink
This example shows how to include a simple fully connected neural network in a Simulink® model that predicts calorie burn when given five time steps of sensor readings from a smart watch.
Battery State of Charge Estimation Using Deep Learning
Define requirements, prepare data, train deep learning networks, verify robustness, integrate networks into Simulink, and deploy models. (自 R2024b 起)
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®.

强化学习

Control Water Level in a Tank Using a DDPG 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.

Python 协同执行

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.
Predict Responses Using TensorFlow Model Predict Block
Predict Responses Using TensorFlow Model Predict block.
Predict Responses Using ONNX Model Predict Block
Predict Responses Using ONNX Model Predict block.
Predict Responses Using PyTorch Model Predict Block
Predict Responses Using PyTorch Model Predict block.
Predict Responses Using Custom Python Model in Simulink (Statistics and Machine Learning Toolbox)
This example shows how to use the Custom Python Model Predict (Statistics and Machine Learning Toolbox) block for prediction in Simulink®.

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)

精选示例

Lane and Vehicle Detection in Simulink Using Deep Learning

Use deep convolutional neural networks inside a Simulink® model to perform lane and vehicle detection. This example takes the frames from a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame.

打开脚本

Classify ECG Signals in Simulink Using Deep Learning

Use wavelet transforms and a deep learning network within a Simulink (R) model to classify ECG signals. This example uses the pretrained convolutional neural network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

打开脚本

Time Series Prediction in Simulink Using Deep Learning Network

Use an LSTM deep learning network inside a Simulink® model to predict the remaining useful life (RUL) of an engine. You include the network inside the Simulink model by using a Stateful Predict block, which predicts the RUL at every simulation step.

打开实时脚本

Battery State of Charge Estimation in Simulink Using Feedforward Neural Network

Use a feedforward deep learning network inside a Simulink® model to predict the state of charge (SOC) of a battery. You include the network inside the Simulink model by using a Predict block, which predicts the SOC at every simulation step.