Deep Learning con Simulink
R2026aImplementare la funzionalità di Deep Learning nei modelli di Simulink® utilizzando i blocchi dalle librerie di blocchi Deep Neural Networks, Python Neural Networks e Deep Learning Layers 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™.
Per generare un modello Simulink che utilizza la libreria di blocchi Deep Learning Layers per rappresentare una rete, utilizzare la funzione exportNetworkToSimulink.
In Simulink, alcune funzionalità di Deep Learning utilizzano un blocco MATLAB Function che richiede un compiler 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.
Funzioni
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (Da R2024b) |
Blocchi
 | Classify | Classify data using a trained deep learning neural network |
 | 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 |
 | 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) |
 | Clipped ReLU Layer | Clipped Rectified Linear Unit (ReLU) layer (Da R2024b) |
 | GELU Layer | Gaussian error linear unit (GELU) layer (Da R2024b) |
 | Leaky ReLU Layer | Leaky rectified linear unit (ReLU) layer (Da R2024b) |
 | PReLU Layer | Parameterized rectified linear unit (PReLU) layer (Da R2026a) |
 | ReLU Layer | Rectified linear unit (ReLU) layer (Da R2024b) |
 | Sigmoid Layer | Sigmoid layer (Da R2024b) |
 | Softmax Layer | Softmax layer (Da R2024b) |
 | Swish Layer | Swish layer (Da R2026a) |
 | Tanh Layer | Hyperbolic tangent (tanh) layer (Da R2024a) |
 | Addition Layer | Addition layer (Da R2024b) |
 | Concatenation Layer | Concatenation layer (Da R2024b) |
 | Depth Concatenation Layer | Depth concatenation layer (Da R2024b) |
 | Multiplication Layer | Multiplication layer (Da R2024b) |
 | Convolution 1D Layer | 1-D convolutional layer (Da R2024b) |
 | Convolution 2D Layer | 2-D convolutional layer (Da R2024b) |
 | Convolution 3D Layer | 3-D convolutional layer (Da R2024b) |
 | Fully Connected Layer | Fully connected layer (Da R2024b) |
 | Rescale-Symmetric 1D | 1-D input layer with rescale-symmetric normalization (Da R2024b) |
 | Rescale-Symmetric 2D | 2-D input layer with rescale-symmetric normalization (Da R2024b) |
 | Rescale-Symmetric 3D | 3-D input layer with rescale-symmetric normalization (Da R2024b) |
 | Rescale-Zero-One 1D | 1-D input layer with rescale-zero-one normalization (Da R2024b) |
 | Rescale-Zero-One 2D | 2-D input layer with rescale-zero-one normalization (Da R2024b) |
 | Rescale-Zero-One 3D | 3-D input layer with rescale-zero-one normalization (Da R2024b) |
 | Zerocenter 1D | 1-D input layer with zerocenter normalization (Da R2024b) |
 | Zerocenter 2D | 2-D input layer with zerocenter normalization (Da R2024b) |
 | Zerocenter 3D | 3-D input layer with zerocenter normalization (Da R2024b) |
 | Zscore 1D | 1-D input layer with zscore normalization (Da R2024b) |
 | Zscore 2D | 2-D input layer with zscore normalization (Da R2024b) |
 | Zscore 3D | 3-D input layer with zscore normalization (Da R2024b) |
 | Batch Normalization Layer | Batch normalization layer (Da R2024b) |
 | Instance Normalization Layer | Instance normalization layer (Da R2026a) |
 | Layer Normalization Layer | Layer normalization layer (Da R2024b) |
 | Inverse Zerocenter | Inverse zero-center normalization (Da R2026a) |
 | Inverse Zscore | Inverse Z-score normalization (Da R2026a) |
 | Average Pooling 1D Layer | 1-D average pooling layer (Da R2024b) |
 | Average Pooling 2D Layer | 2-D average pooling layer (Da R2024b) |
 | Average Pooling 3D Layer | 3-D average pooling layer (Da R2024b) |
 | Global Average Pooling 1D Layer | 1-D global average pooling layer (Da R2024b) |
 | Global Average Pooling 2D Layer | 2-D global average pooling layer (Da R2024b) |
 | Global Average Pooling 3D Layer | 3-D global average pooling layer (Da R2024b) |
 | Global Max Pooling 1D Layer | 1-D global max pooling layer (Da R2024b) |
 | Global Max Pooling 2D Layer | 2-D global max pooling layer (Da R2024b) |
 | Global Max Pooling 3D Layer | 3-D global max pooling layer (Da R2024b) |
 | Max Pooling 1D Layer | 1-D max pooling layer (Da R2024b) |
 | Max Pooling 2D Layer | 2-D max pooling layer (Da R2024b) |
 | Max Pooling 3D Layer | 3-D max pooling layer (Da R2024b) |
 | Flatten Layer | Flatten layer (Da R2024b) |
 | GRU Layer | Gated recurrent unit (GRU) layer for recurrent neural network (RNN) (Da R2025a) |
 | GRU Projected Layer | Gated recurrent unit (GRU) projected layer for recurrent neural network (RNN) (Da R2025a) |
 | LSTM Layer | Long short-term memory (LSTM) layer for recurrent neural network (RNN) (Da R2024b) |
 | LSTM Projected Layer | Long short-term memory (LSTM) projected layer for recurrent neural network (RNN) (Da R2024b) |
 | Dropout Layer | Dropout layer (Da R2024b) |
 | Identity Layer | Identity layer (Da R2026a) |
 | Scaling Layer | Scaling layer (Da R2026a) |
 | Spatial Dropout Layer | Spatial dropout layer (Da R2026a) |
Argomenti
Blocchi dei livelli di Deep Learning
- 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
Learn how to implement unsupported deep learning layer blocks.
Immagini
- Classify Image in Simulink Using SqueezeNet
This example shows how to classify an image in Simulink® using the Classify 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 Predictblock. - 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 Classifyblock. - 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. (Da R2024b) - 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. (Da 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. - Classify Motor Faults Using Deep Learning
This example shows how to train a deep learning model to classify faults in a permanent magnet synchronous motor (PMSM) using simulated data across various revolutions per minute (RPM). (Da R2025a) - 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 (Reinforcement Learning Toolbox)
Use DDPG agent to control water level in a tank modeled in Simulink. - Train DDPG Agent for Adaptive Cruise Control (Reinforcement Learning Toolbox)
Train a DDPG agent for an adaptive cruise control application. - Train DQN Agent for Lane Keeping Assist Using Parallel Computing (Reinforcement Learning Toolbox)
Train a DQN agent for an automated driving application using parallel computing. - Train DDPG Agent for Path-Following Control (Reinforcement Learning Toolbox)
Train a DDPG agent for lane following control.
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)
Esempi in primo piano
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.
Profile Deep Learning Network for Deployment
Profile a deep learning network in Simulink. Profiling your network before training is a good way of checking that the network meets your requirements, before spending time on training. This example shows how to check:
