Deep Learning con Simulink
Implementare la funzionalità di Deep Learning nei modelli Simulink® utilizzando i blocchi dalla libreria di blocchi Deep Neural Networks (Reti neurali profonde), compresa in Deep Learning Toolbox™ o utilizzando il blocco Deep Learning Object Detector dalla libreria di blocchi Analysis & Enhancement (Analisi e miglioramento) compresa in 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 |
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 |
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
- 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.
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