Deep learning con Simulink
Implemente la funcionalidad de deep learning en modelos de Simulink® usando bloques de las bibliotecas de bloques Deep Neural Networks, Python Neural Networks y Deep Learning Layers (incluidas en Deep Learning Toolbox™) o usando el bloque Deep Learning Object Detector de la biblioteca de bloques Analysis & Enhancement (incluida en Computer Vision Toolbox™).
Para generar un modelo de Simulink que usa la biblioteca de bloques Deep Learning Layers para representar una red, utilice la función exportNetworkToSimulink.
Algunas funcionalidades de deep learning en Simulink usan un bloque MATLAB Function, que requiere un compilador compatible. La mayoría de plataformas incluyen un compilador de C predeterminado con la instalación de MATLAB®. Cuando usa el lenguaje C++, debe instalar un compilador de C++ compatible. Para ver una lista de los compiladores compatibles, abra Supported and Compatible Compilers (Compiladores admitidos y compatibles), haga clic en la pestaña correspondiente a su sistema operativo, busque la tabla Simulink Product Family (Familia de productos Simulink) y vaya a la columna For Model Referencing, Accelerator mode, Rapid Accelerator mode, and MATLAB Function blocks (Para referencias de modelos, modo Acelerador, modo Acelerador rápido y bloques MATLAB Function). Si tiene instalados varios compiladores compatibles con MATLAB en su sistema, puede cambiar el compilador predeterminado usando el comando mex -setup. Consulte Cambiar el compilador predeterminado.
Funciones
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (Desde R2024b) |
Bloques
Temas
Bloques Deep Learning Layer
- 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
This example shows how to implement layers using Simulink blocks or MATLAB code in a MATLAB Function block.
Imágenes
- Clasificar imágenes en Simulink con GoogLeNet
En este ejemplo se muestra cómo clasificar una imagen en Simulink® usando el bloqueImage Classifier. - 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.
Secuencias
- Predecir y actualizar el estado de una red en Simulink
En este ejemplo se muestra cómo predecir respuestas de una red neuronal recurrente entrenada en Simulink® mediante el bloqueStateful Predict. - Clasificar y actualizar el estado de una red en Simulink
En este ejemplo se muestra cómo clasificar datos de una red neuronal recurrente entrenada en Simulink® mediante el bloqueStateful Classify. - 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. (Desde 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. - 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
Coejecución de 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®.
Generación de código
- Generación de código de deep learning a partir de aplicaciones de Simulink
Genere código C/C++ y GPU para despliegues en objetivos de escritorio o embebidos - Export Network to FMU
This example shows how to export a trained network as a Functional Mock-up Unit (FMU). (Desde R2023b)
Ejemplos destacados
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.
