Modeling and Prediction with NARX and Time-Delay Networks
Solve time series problems using dynamic neural networks, including networks with feedback
|Neural Net Time Series||Solve nonlinear time series problem using dynamic neural networks|
|Time delay neural network|
|Nonlinear autoregressive neural network with external input|
|Nonlinear autoregressive neural network|
|Layer recurrent neural network|
|Distributed delay network|
|Train shallow neural network|
|Generate Simulink block for shallow neural network simulation|
|Add delay to neural network response|
|Remove delay to neural network’s response|
|Convert neural network open-loop feedback to closed loop|
|Convert neural network closed-loop feedback to open loop|
|Plot error histogram|
|Plot input to error time-series cross-correlation|
|Plot linear regression|
|Plot dynamic network time series response|
|Plot autocorrelation of error time series|
|Generate MATLAB function for simulating shallow neural network|
Examples and How To
- 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.
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.
- 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
- 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.