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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

Apps

Neural Net Time Series Solve a nonlinear time series problem by training a dynamic neural network

Functions

nnstart Neural network getting started GUI
view View 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 neural network
gensim Generate Simulink block for 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 neural network

Examples and How To

Basic Design

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 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

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.

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.

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