Deep Learning con Simulink
Implementare la funzionalità di Deep Learning nei modelli di Simulink® utilizzando i blocchi dalle librerie di blocchi Deep Neural Networks e Python Neural Networks, incluse nella Deep Learning Toolbox™ o utilizzando il blocco Deep Learning Object Detector dalla libreria di blocchi Analysis & Enhancement (Analisi e miglioramento) inclusa nella Computer Vision Toolbox™.
La funzionalità di Deep Learning in Simulink utilizza il blocco MATLAB Function che richiedere un compilatore supportato. Per la maggior parte delle piattaforme, un compilatore C predefinito viene fornito con l'installazione di MATLAB®. Quando si utilizza il linguaggio C++, è necessario installare un compilatore C++. Per visualizzare un elenco dei compilatori supportati, aprire Supported and Compatible Compilers (Compilatori supportati e compatibili), fare clic sulla scheda corrispondente al sistema operativo in uso, individuare la tabella Simulink Product Family e andare alla colonna For Model Referencing, Accelerator mode, Rapid Accelerator mode, and MATLAB Function blocks. Se sul sistema sono installati più compilatori supportati da MATLAB, è possibile modificare il compilatore predefinito utilizzando il comando mex -setup
. Vedere Change Default Compiler.
Blocchi
Image Classifier | Classifica i dati utilizzando una rete neurale addestrata di Deep Learning (Da R2020b) |
Predict | Predict responses using a trained deep learning neural network (Da R2020b) |
Stateful Classify | Classify data using a trained deep learning recurrent neural network (Da R2021a) |
Stateful Predict | Predict responses using a trained recurrent neural network (Da R2021a) |
Deep Learning Object Detector | Detect objects using trained deep learning object detector (Da R2021b) |
TensorFlow Model Predict | Predict responses using pretrained Python TensorFlow model (Da R2024a) |
PyTorch Model Predict | Predict responses using pretrained Python PyTorch model (Da R2024a) |
ONNX Model Predict | Predict responses using pretrained Python ONNX model (Da R2024a) |
Custom Python Model Predict | Predict responses using pretrained custom Python model (Da R2024a) |
Argomenti
Immagini
- 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.
Sequenze
- 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. - 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®.
Apprendimento di rinforzo
- 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.
Esecuzione simultanea di 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®.
Generazione di codice
- Generazione di codice di Deep Learning dalle applicazioni Simulink
Generare codice C/C++ e GPU per la distribuzione su target desktop o integrati - Export Network to FMU
This example shows how to export a trained network as a Functional Mock-up Unit (FMU). (Da R2023b)