Main Content

Function Approximation and Nonlinear Regression

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


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


nftoolNeural net fitting tool
viewView shallow neural network
fitnetFunction fitting neural network
feedforwardnetGenerate feedforward neural network
cascadeforwardnetCascade-forward neural network
trainTrain shallow neural network
trainlmLevenberg-Marquardt backpropagation
trainbrBayesian regularization backpropagation
trainscgScaled conjugate gradient backpropagation
trainrpResilient backpropagation
mseMean squared normalized error performance function
regression(Not recommended) Perform linear regression of shallow network outputs on targets
ploterrhistPlot error histogram
plotfitPlot function fit
plotperformPlot network performance
plotregressionPlot linear regression
plottrainstatePlot training state values
genFunctionGenerate MATLAB function for simulating shallow neural network

Examples and How To

Basic Design

Fit Data with a Shallow Neural Network

Train a shallow neural network to fit a data set.

Create, Configure, and Initialize Multilayer Shallow Neural Networks

Prepare a multilayer shallow neural network.

Body Fat Estimation

This example illustrates how a function fitting neural network can estimate body fat percentage based on anatomical measurements.

Train and Apply Multilayer Shallow Neural Networks

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

Analyze Shallow Neural Network Performance After Training

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

Deploy Shallow Neural Network Functions

Simulate and deploy trained shallow neural networks using MATLAB® tools.

Deploy Training of Shallow Neural Networks

Learn how to deploy training of shallow neural networks.

Training Scalability and Efficiency

Shallow 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

Choose Neural Network Input-Output Processing Functions

Preprocess inputs and targets for more efficient training.

Configure Shallow 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 Shallow 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.


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 functionality.

Multilayer Shallow Neural Networks and Backpropagation Training

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

Multilayer Shallow Neural Network Architecture

Learn the architecture of a multilayer shallow neural network.

Understanding Shallow Network Data Structures

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

Sample Data Sets for Shallow Neural Networks

List of sample data sets to use when experimenting with shallow neural 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.