Deep Learning with Simulink

Extend deep learning workflows using Simulink

Implement deep learning functionality in Simulink^® models by using blocks from the Deep Neural Networks block library, included in the Deep Learning Toolbox™, or by using the Deep Learning Object Detector block from the Analysis & Enhancement block library included in the Computer Vision Toolbox™.

Deep learning functionality in Simulink uses MATLAB Function block that requires a supported compiler. For most platforms, a default C compiler is supplied with the MATLAB^® installation. When using C++ language, you must install a compatible C++ compiler. To see a list of supported compilers, open Supported and Compatible Compilers, click the tab that corresponds to your operating system, find the Simulink Product Family table, and go to the For Model Referencing, Accelerator mode, Rapid Accelerator mode, and MATLAB Function blocks column. If you have multiple MATLAB-supported compilers installed on your system, you can change the default compiler using the mex -setup command. See Change Default Compiler.

Blöcke

Image Classifier	Classify data using a trained deep learning neural network (Seit R2020b)
Predict	Predict responses using a trained deep learning neural network (Seit R2020b)
Stateful Classify	Classify data using a trained deep learning recurrent neural network (Seit R2021a)
Stateful Predict	Predict responses using a trained recurrent neural network (Seit R2021a)
Deep Learning Object Detector	Detect objects using trained deep learning object detector (Seit R2021b)

Themen

Images

Classify Images in Simulink Using GoogLeNet
This example shows how to classify an image in Simulink® using the Image 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 using importTensorFlowNetwork, and then use the Predict block for image classification in Simulink.

Sequences

Predict and Update Network State in Simulink
This example shows how to predict responses for a trained recurrent neural network in Simulink® by using the Stateful 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 the Stateful 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.
Battery State of Charge Workflow
An example workflow for training, compressing, and using a deep learning network in Simulink.
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

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.

Code Generation

Deep Learning Code Generation from Simulink Applications
Generate C/C++ and GPU code for deployment on desktop or embedded targets
Export Network to FMU
This example shows how to export a trained network as a Functional Mock-up Unit (FMU). (Seit R2023b)

Enthaltene Beispiele

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 Deep Learning 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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