Main Content

Modeling and Prediction with NARX and Time-Delay Networks

Solve time series problems using dynamic neural networks, including networks with feedback


Neural Net Time SeriesSolve nonlinear time series problem using dynamic neural networks


ntstoolNeural network time series tool
viewView shallow neural network
timedelaynetTime delay neural network
narxnetNonlinear autoregressive neural network with external input
narnetNonlinear autoregressive neural network
layrecnetLayer recurrent neural network
distdelaynetDistributed delay network
trainTrain shallow neural network
gensimGenerate Simulink block for shallow neural network simulation
adddelayAdd delay to neural network response
removedelayRemove delay to neural network’s response
closeloopConvert neural network open-loop feedback to closed loop
openloopConvert neural network closed-loop feedback to open loop
ploterrhistPlot error histogram
plotinerrcorrPlot input to error time-series cross-correlation
plotregressionPlot linear regression
plotresponsePlot dynamic network time series response
ploterrcorrPlot autocorrelation of error time series
genFunctionGenerate MATLAB function for simulating shallow neural network

Examples and How To

Basic Design

Shallow Neural Network Time-Series Prediction and Modeling

Make a time series prediction using the Neural Network Time Series App and command-line functions.

Design Time Series Time-Delay Neural Networks

Learn to design focused time-delay neural network (FTDNN) for time-series prediction.

Multistep Neural Network Prediction

Learn multistep neural network prediction.

Design Time Series NARX Feedback Neural Networks

Create and train a nonlinear autoregressive network with exogenous inputs (NARX).

Design Layer-Recurrent Neural Networks

Create and train a dynamic network that is a Layer-Recurrent Network (LRN).

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.

Maglev Modeling

This example illustrates how a NARX (Nonlinear AutoRegressive with eXternal input) neural network can model a magnet levitation dynamical system.

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.


How Dynamic Neural Networks Work

Learn how feedforward and recurrent networks work.

Multiple Sequences with Dynamic Neural Networks

Manage time-series data that is available in several short sequences.

Neural Network Time-Series Utilities

Learn how to use utility functions to manipulate neural network data.

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.