Neural network models are structured as a series of layers that reflect the way the brain processes information. The regression neural network models available in Statistics and Machine Learning Toolbox™ are fully connected, feedforward neural networks for which you can adjust the size of the fully connected layers and change the activation functions of the layers.
To train a regression neural network model, use the Regression Learner app. For greater
flexibility, train a regression neural network model using
fitrnet in the command-line interface. After training, you can
predict responses for new data by passing the model and the new predictor data
If you want to create more complex deep learning networks and have Deep Learning Toolbox™, you can try the Deep Network Designer (Deep Learning Toolbox) app.
|Regression Learner||Train regression models to predict data using supervised machine learning|
|RegressionNeuralNetwork Predict||Predict responses using neural network regression model (Since R2021b)|
Create Neural Network Model
- Assess Regression Neural Network Performance
fitrnetto create a feedforward regression neural network model with fully connected layers, and assess the performance of the model on test data.
- Train Regression Neural Networks Using Regression Learner App
Create and compare regression neural networks, and export trained models to make predictions for new data.
- Deploy Neural Network Regression Model to FPGA/ASIC Platform
Predict in Simulink® using a neural network regression model, and deploy the Simulink model to an FPGA/ASIC platform by using HDL code generation.