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simulink 深度学习 -凯发k8网页登录

使用 simulink 扩展深度学习工作流

通过使用 deep learning toolbox™ 中包含的 deep neural networks 模块库中的模块，或使用 computer vision toolbox™ 中包含的 analysis & enhancement 模块库中的 deep learning object detector 模块，在 simulink® 模型中实现深度学习功能。

simulink 中的深度学习功能使用需要支持的编译器的 matlab function 模块。对于大多数平台，会随 matlab® 安装提供一个默认的 c 编译器。使用 c 语言时，必须安装兼容的 c 编译器。要查看支持的编译器列表，请打开，点击与您的操作系统对应的选项卡，找到 simulink product family 表，并转至 for model referencing, accelerator mode, rapid accelerator mode, and matlab function blocks 列。如果您的系统上安装了多个 matlab 支持的编译器，可以使用 mex -setup 命令更改默认编译器。请参阅更改默认编译器。

模块

使用经过训练的深度学习神经网络对数据进行分类
predict responses using a trained deep learning neural network
classify data using a trained deep learning recurrent neural network
predict responses using a trained recurrent neural network
detect objects using trained deep learning object detector

主题

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  • this example shows how to classify an image in simulink® using the image classifier block.

  • improve simulation speed with accelerator and rapid accelerator modes.

  • this example shows how to use deep convolutional neural networks inside a simulink® model to perform lane and vehicle detection.

  • this example shows how to use wavelet transforms and a deep learning network within a simulink (r) model to classify ecg signals.

  • import a pretrained tensorflow™ network using importtensorflownetwork, and then use the predict block for image classification in simulink.

序列


  • this example shows how to predict responses for a trained recurrent neural network in simulink® by using the stateful predict block.

  • this example shows how to classify data for a trained recurrent neural network in simulink® by using the stateful classify block.

  • detect the presence of speech commands in audio using a simulink model.

  • 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.

  • 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.

  • this example shows how to use code generation to improve the performance of deep learning simulations in simulink®.

增强学习


  • train a controller using reinforcement learning with a plant modeled in simulink as the training environment.

  • train a reinforcement learning agent for an adaptive cruise control application.

  • train a reinforcement learning agent for a lane keeping assist application.

  • train a reinforcement learning agent for a lane following application.

代码生成


  • 生成用于在桌面或嵌入式目标上部署的 c/c 和 gpu 代码
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