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Deep Learning avec Simulink
R2026aImplémentez des fonctionnalités de Deep Learning dans des modèles Simulink® avec des blocs des bibliothèques Deep Neural Networks, Python Neural Networks et Deep Learning Layers incluses dans Deep Learning Toolbox™ ou en utilisant le bloc Deep Learning Object Detector de la bibliothèque Analysis & Enhancement incluse dans Computer Vision Toolbox™.
Pour générer un modèle Simulink qui utilise la bibliothèque de blocs Deep Learning Layers afin de représenter un réseau, utilisez la fonction exportNetworkToSimulink.
Certaines fonctionnalités de Deep Learning dans Simulink utilisent le bloc MATLAB Function qui nécessite un compilateur supporté. Pour la plupart des plateformes, un compilateur C par défaut est fourni avec l’installation MATLAB®. Si vous utilisez le langage C++, vous devez installer un compilateur C++ compatible. Pour voir une liste des compilateurs supportés, ouvrez Compilateurs supportés et compatibles, cliquez sur l’onglet qui correspond à votre système d’exploitation, trouvez la table Simulink Product Family et allez dans la colonne For Model Referencing, Accelerator mode, Rapid Accelerator mode, and MATLAB Function blocks. Si vous avez plusieurs compilateurs compatibles MATLAB installés sur votre système, vous pouvez modifier le compilateur par défaut avec la commande mex -setup. Veuillez consulter Change Default Compiler.
Fonctions
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (depuis R2024b) |
Blocs
 | Classify | Classer des données avec un réseau de neurones entraîné pour le Deep Learning |
 | Predict | Predict responses using a trained deep learning neural network |
 | Stateful Classify | Classify data using a trained deep learning recurrent neural network |
 | Stateful Predict | Predict responses using a trained recurrent neural network |
 | Deep Learning Object Detector | Detect objects using trained deep learning object detector |
 | TensorFlow Model Predict | Predict responses using pretrained Python TensorFlow model (depuis R2024a) |
 | PyTorch Model Predict | Predict responses using pretrained Python PyTorch model (depuis R2024a) |
 | ONNX Model Predict | Predict responses using pretrained Python ONNX model (depuis R2024a) |
 | Custom Python Model Predict | Predict responses using pretrained custom Python model (depuis R2024a) |
 | Clipped ReLU Layer | Clipped Rectified Linear Unit (ReLU) layer (depuis R2024b) |
 | GELU Layer | Gaussian error linear unit (GELU) layer (depuis R2024b) |
 | Leaky ReLU Layer | Leaky rectified linear unit (ReLU) layer (depuis R2024b) |
 | PReLU Layer | Parameterized rectified linear unit (PReLU) layer (depuis R2026a) |
 | ReLU Layer | Rectified linear unit (ReLU) layer (depuis R2024b) |
 | Sigmoid Layer | Sigmoid layer (depuis R2024b) |
 | Softmax Layer | Softmax layer (depuis R2024b) |
 | Swish Layer | Swish layer (depuis R2026a) |
 | Tanh Layer | Hyperbolic tangent (tanh) layer (depuis R2024a) |
 | Addition Layer | Addition layer (depuis R2024b) |
 | Concatenation Layer | Concatenation layer (depuis R2024b) |
 | Depth Concatenation Layer | Depth concatenation layer (depuis R2024b) |
 | Multiplication Layer | Multiplication layer (depuis R2024b) |
 | Convolution 1D Layer | 1-D convolutional layer (depuis R2024b) |
 | Convolution 2D Layer | 2-D convolutional layer (depuis R2024b) |
 | Convolution 3D Layer | 3-D convolutional layer (depuis R2024b) |
 | Fully Connected Layer | Fully connected layer (depuis R2024b) |
 | Rescale-Symmetric 1D | 1-D input layer with rescale-symmetric normalization (depuis R2024b) |
 | Rescale-Symmetric 2D | 2-D input layer with rescale-symmetric normalization (depuis R2024b) |
 | Rescale-Symmetric 3D | 3-D input layer with rescale-symmetric normalization (depuis R2024b) |
 | Rescale-Zero-One 1D | 1-D input layer with rescale-zero-one normalization (depuis R2024b) |
 | Rescale-Zero-One 2D | 2-D input layer with rescale-zero-one normalization (depuis R2024b) |
 | Rescale-Zero-One 3D | 3-D input layer with rescale-zero-one normalization (depuis R2024b) |
 | Zerocenter 1D | 1-D input layer with zerocenter normalization (depuis R2024b) |
 | Zerocenter 2D | 2-D input layer with zerocenter normalization (depuis R2024b) |
 | Zerocenter 3D | 3-D input layer with zerocenter normalization (depuis R2024b) |
 | Zscore 1D | 1-D input layer with zscore normalization (depuis R2024b) |
 | Zscore 2D | 2-D input layer with zscore normalization (depuis R2024b) |
 | Zscore 3D | 3-D input layer with zscore normalization (depuis R2024b) |
 | Batch Normalization Layer | Batch normalization layer (depuis R2024b) |
 | Instance Normalization Layer | Instance normalization layer (depuis R2026a) |
 | Layer Normalization Layer | Layer normalization layer (depuis R2024b) |
 | Inverse Zerocenter | Inverse zero-center normalization (depuis R2026a) |
 | Inverse Zscore | Inverse Z-score normalization (depuis R2026a) |
 | Average Pooling 1D Layer | 1-D average pooling layer (depuis R2024b) |
 | Average Pooling 2D Layer | 2-D average pooling layer (depuis R2024b) |
 | Average Pooling 3D Layer | 3-D average pooling layer (depuis R2024b) |
 | Global Average Pooling 1D Layer | 1-D global average pooling layer (depuis R2024b) |
 | Global Average Pooling 2D Layer | 2-D global average pooling layer (depuis R2024b) |
 | Global Average Pooling 3D Layer | 3-D global average pooling layer (depuis R2024b) |
 | Global Max Pooling 1D Layer | 1-D global max pooling layer (depuis R2024b) |
 | Global Max Pooling 2D Layer | 2-D global max pooling layer (depuis R2024b) |
 | Global Max Pooling 3D Layer | 3-D global max pooling layer (depuis R2024b) |
 | Max Pooling 1D Layer | 1-D max pooling layer (depuis R2024b) |
 | Max Pooling 2D Layer | 2-D max pooling layer (depuis R2024b) |
 | Max Pooling 3D Layer | 3-D max pooling layer (depuis R2024b) |
 | Flatten Layer | Flatten layer (depuis R2024b) |
 | GRU Layer | Gated recurrent unit (GRU) layer for recurrent neural network (RNN) (depuis R2025a) |
 | GRU Projected Layer | Gated recurrent unit (GRU) projected layer for recurrent neural network (RNN) (depuis R2025a) |
 | LSTM Layer | Long short-term memory (LSTM) layer for recurrent neural network (RNN) (depuis R2024b) |
 | LSTM Projected Layer | Long short-term memory (LSTM) projected layer for recurrent neural network (RNN) (depuis R2024b) |
 | Dropout Layer | Dropout layer (depuis R2024b) |
 | Identity Layer | Identity layer (depuis R2026a) |
 | Scaling Layer | Scaling layer (depuis R2026a) |
 | Spatial Dropout Layer | Spatial dropout layer (depuis R2026a) |
Rubriques
Blocs pour les couches de Deep Learning
- List of Deep Learning Layer Blocks and Subsystems
Discover all the deep learning layer blocks and subsystems in Simulink. - Implement Unsupported Deep Learning Layer Blocks
Learn how to implement unsupported deep learning layer blocks.
Images
- Classify Image in Simulink Using SqueezeNet
This example shows how to classify an image in Simulink® using the Classify 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.
Séquences
- 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 Predictblock. - 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 Classifyblock. - 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. (depuis R2024b) - 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. (depuis R2024b) - 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. - Classify Motor Faults Using Deep Learning
This example shows how to train a deep learning model to classify faults in a permanent magnet synchronous motor (PMSM) using simulated data across various revolutions per minute (RPM). (depuis R2025a) - 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®.
Reinforcement Learning
- Control Water Level in a Tank Using a DDPG Agent (Reinforcement Learning Toolbox)
Use DDPG agent to control water level in a tank modeled in Simulink. - Train DDPG Agent for Adaptive Cruise Control (Reinforcement Learning Toolbox)
Train a DDPG agent for an adaptive cruise control application. - Train DQN Agent for Lane Keeping Assist Using Parallel Computing (Reinforcement Learning Toolbox)
Train a DQN agent for an automated driving application using parallel computing. - Train DDPG Agent for Path-Following Control (Reinforcement Learning Toolbox)
Train a DDPG agent for lane following control.
Coexécution avec Python
- 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®.
Génération de code
- Génération de code de Deep Learning depuis des applications Simulink
Générer du code C/C++ et GPU pour le déploiement sur un ordinateur ou des cibles embarquées - Export Network to FMU
This example shows how to export a trained network as a Functional Mock-up Unit (FMU). (depuis R2023b)
Sélection d՚exemples
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.
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).
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.
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.
Profile Deep Learning Network for Deployment
Profile a deep learning network in Simulink. Profiling your network before training is a good way of checking that the network meets your requirements, before spending time on training. This example shows how to check:
