Documentation

This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Function Approximation and Nonlinear Regression

Create a neural network to generalize nonlinear relationships between example inputs and outputs

Apps

Neural Net Fitting Fit data by training a two-layer feed-forward network

Functions

nnstart Neural network getting started GUI
view View neural network
fitnet Function fitting neural network
feedforwardnet Feedforward neural network
cascadeforwardnet Cascade-forward neural network
train Train neural network
trainlm Levenberg-Marquardt backpropagation
trainbr Bayesian regularization backpropagation
trainscg Scaled conjugate gradient backpropagation
trainrp Resilient backpropagation
mse Mean squared normalized error performance function
regression Linear regression
ploterrhist Plot error histogram
plotfit Plot function fit
plotperform Plot network performance
plotregression Plot linear regression
plottrainstate Plot training state values
genFunction Generate MATLAB function for simulating neural network

Examples and How To

Basic Design

Fit Data with a Neural Network

Train a neural network to fit a data set.

Create, Configure, and Initialize Multilayer Neural Networks

Prepare a multilayer neural network.

Train and Apply Multilayer Neural Networks

Train and use a multilayer network for function approximation or pattern recognition.

Analyze Neural Network Performance After Training

Analyze network performance and adjust training process, network architecture, or data.

Deploy Trained Neural Network Functions

Simulate and deploy trained neural networks using MATLAB® tools.

Deploy Training of Neural Networks

Use MATLAB Runtime to deploy functions that can train a model.

Training Scalability and Efficiency

Neural Networks with Parallel and GPU Computing

Use parallel and distributed computing to speed up neural network training and simulation and handle large data.

Automatically Save Checkpoints During Neural Network Training

Save intermediate results to protect the value of long training runs.

Optimize Neural Network Training Speed and Memory

Make neural network training more efficient.

Optimal Solutions

Representing Unknown or Don't-Care Targets

Prevent unknown target values from impacting training.

Choose Neural Network Input-Output Processing Functions

Preprocess inputs and targets for more efficient training.

Configure Neural Network Inputs and Outputs

Learn how to manually configure the network before training using the configure function.

Divide Data for Optimal Neural Network Training

Use functions to divide the data into training, validation, and test sets.

Choose a Multilayer Neural Network Training Function

Comparison of training algorithms on different problem types.

Improve Neural Network Generalization and Avoid Overfitting

Learn methods to improve generalization and prevent overfitting.

Train Neural Networks with Error Weights

Learn how to use error weighting when training neural networks.

Normalize Errors of Multiple Outputs

Learn how to fit output elements with different ranges of values.

Concepts

Workflow for Neural Network Design

Learn the primary steps in a neural network design process.

Four Levels of Neural Network Design

Learn the different levels of using Neural Network Toolbox functionality.

Multilayer Neural Networks and Backpropagation Training

Workflow for designing a multilayer feedforward neural network for function fitting and pattern recognition.

Multilayer Neural Network Architecture

Learn the architecture of a multilayer neural network.

Understanding Neural Network Toolbox Data Structures

Learn how the format of input data structures affects the simulation of networks.

Neural Network Object Properties

Learn properties that define the basic features of a network.

Neural Network Subobject Properties

Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.

Was this topic helpful?