Modellierung und Vorhersage mit NARX und Time-Delay-Netzen
Lösung von Zeitreihenproblemen mit dynamischen neuronalen Netzen, einschließlich Netzen mit Feedback
Apps
| Neural Net Time Series | Lösung von nichtlinearen Zeitreihenproblemen mit dynamischen neuronalen Netzen |
Funktionen
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 | Trainieren von flachen neuronalen Netzen |
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 | Plotten von linearer Regression |
plotresponse | Plot dynamic network time series response |
ploterrcorr | Plot autocorrelation of error time series |
genFunction | Generate MATLAB function for simulating shallow neural network |
Beispiele und Gewusst wie
Grundlegendes Design
- Zeitreihenvorhersage und Modellierung mit flachen neuronalen Netzen
Durchführen von Zeitreihenvorhersagen mithilfe der App „Neural Net Time Series“ und Befehlszeilenfunktionen. - 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.
Skalierbarkeit und Effizienz des Trainings
- 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.
Optimale Lösungen
- 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 theconfigurefunction. - 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.
Konzepte
- 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.
- Beispieldatensätze für flache neuronale Netze
Liste der Beispieldatensätze, die zum Experimentieren mit flachen neuronalen Netzen verwendet werden können.
- 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.
