Regression
Verwenden Sie Deep-Learning-Techniken, um Signale zu entrauschen. Verwenden Sie differenzierbare Zeit-Frequenz-Transformationen, um Signale zu rekonstruieren, wenn Informationen fehlen.
Apps
| Regression Learner | Train regression models to predict data using supervised machine learning |
| Experiment Manager | Create and run experiments to train and compare deep learning networks |
Funktionen
Blöcke
| Wavelet Scattering | Model wavelet scattering network in Simulink (Seit R2022b) |
Verwandte Informationen
Themen
- Use Experiment Manager Templates for Signal Processing Workflows (Signal Processing Toolbox)
Set up and run deep learning experiments for signal segmentation, classification, and regression.
- Signal Segmentation by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification Using Transfer Learning (Signal Processing Toolbox)
- Signal Regression by Sweeping Hyperparameters (Signal Processing Toolbox)
Enthaltene Beispiele
CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimates the pulse parameters using a combination of time-frequency maps and a deep-learning object detector.
Denoise Signals with Generative Adversarial Networks
Use autoencoders and generative adversarial networks to denoise signals.
Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources.
Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression.
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
Denoise Speech Using Deep Learning Networks
Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.
Denoise Signals with Adversarial Learning Denoiser Model
Use an adversarial learning denoiser model to denoise signals.
Signal Recovery with Differentiable Scalograms and Spectrograms
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information.
