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Deep learning con Simulink

Amplíe flujos de trabajo de deep learning con Simulink

Implemente la funcionalidad de deep learning en modelos de Simulink^® con bloques de la biblioteca de bloques Deep Neural Networks (incluida en Deep Learning Toolbox™), o usando el bloque Deep Learning Object Detector de la biblioteca de bloques Analysis & Enhancement (incluida en Computer Vision Toolbox™).

La funcionalidad de deep learning de Simulink usa el 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.

Bloques

Image Classifier	Clasificar datos con una red neuronal de deep learning entrenada (desde R2020b)
Predict	Predecir respuestas usando una red neuronal de deep learning entrenada (desde R2020b)
Stateful Classify	Classify data using a trained deep learning recurrent neural network (desde R2021a)
Stateful Predict	Predict responses using a trained recurrent neural network (desde R2021a)
Deep Learning Object Detector	Detect objects using trained deep learning object detector (desde R2021b)
TensorFlow Model Predict	Predict responses using pretrained Python TensorFlow model (desde R2024a)
PyTorch Model Predict	Predict responses using pretrained Python PyTorch model (desde R2024a)
ONNX Model Predict	Predict responses using pretrained Python ONNX model (desde R2024a)
Custom Python Model Predict	Predict responses using pretrained custom Python model (desde R2024a)

Temas

Imágenes

Clasificar imágenes en Simulink con GoogLeNet
En este ejemplo se muestra cómo clasificar una imagen en Simulink® usando el bloque Image 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 using importTensorFlowNetwork, and then use the Predict block for image classification in Simulink.
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.

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 bloque Stateful 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 bloque Stateful 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.
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.

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.

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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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