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Deep Learning avec Simulink

Enrichir des workflows de Deep Learning avec Simulink

Implémentez des fonctionnalités de Deep Learning dans des modèles Simulink^® avec des blocs des bibliothèques Deep Neural Networks et Python Neural Networks incluses dans Deep Learning Toolbox™ ou avec le bloc Deep Learning Object Detector de la bibliothèque Analysis & Enhancement incluse dans Computer Vision Toolbox™.

Les 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.

Blocs

Image Classifier	Classer des données avec un réseau de neurones entraîné pour le Deep Learning (depuis R2020b)
Predict	Predict responses using a trained deep learning neural network (depuis R2020b)
Stateful Classify	Classify data using a trained deep learning recurrent neural network (depuis R2021a)
Stateful Predict	Predict responses using a trained recurrent neural network (depuis R2021a)
Deep Learning Object Detector	Detect objects using trained deep learning object detector (depuis R2021b)
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)

Rubriques

Images

Classify Images in Simulink Using GoogLeNet
This example shows how to classify an image in Simulink® using the Image 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 using importTensorFlowNetwork, 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 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.
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®.

Reinforcement Learning

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.

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)

Exemples présentés

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.

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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).

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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.

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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.

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