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Modeling and Prediction with NARX and Time-Delay Networks

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

Solve nonlinear time series problem using dynamic neural networks

`ntstool`	Open Neural Net Time Series app
`view`	View shallow neural network

`timedelaynet`	Time delay neural network
`narxnet`	Nonlinear autoregressive neural network with external input
`narnet`	Nonlinear autoregressive neural network
`layrecnet`	Layer recurrent neural network
`distdelaynet`	Distributed delay network

`train`	Train shallow neural network
`gensim`	Generate Simulink block for shallow neural network simulation
`adddelay`	Add delay to neural network response
`removedelay`	Remove delay to neural network’s response
`closeloop`	Convert neural network open-loop feedback to closed loop
`openloop`	Convert neural network closed-loop feedback to open loop
`ploterrhist`	Plot error histogram
`plotinerrcorr`	Plot input to error time-series cross-correlation
`plotregression`	Plot linear regression
`plotresponse`	Plot dynamic network time series response
`ploterrcorr`	Plot autocorrelation of error time series
`genFunction`	Generate MATLAB function for simulating shallow neural network

Shallow Neural Network Time-Series Prediction and Modeling
Make a time series prediction using the Neural Net 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.

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.

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.