Modellazione e previsione con reti NARX e Time-Delay

Risolvere i problemi delle serie temporali utilizzando reti neurali dinamiche, comprese le reti con feedback

App

Risolvere i problemi delle serie temporali non lineari utilizzando reti neurali dinamiche

`ntstool`	Apre l’app Neural Net Time Series
`view`	Visualizzazione della rete neurale superficiale

`timedelaynet`	Time delay neural network
`narxnet`	Rete neurale autoregressiva non lineare con input esterno
`narnet`	Nonlinear autoregressive neural network
`layrecnet`	Rete neurale ricorrente a livelli
`distdelaynet`	Distributed delay network

`train`	Addestra una rete neurale superficiale
`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`	Traccia la regressione lineare
`plotresponse`	Plot dynamic network time series response
`ploterrcorr`	Plot autocorrelation of error time series
`genFunction`	Generate MATLAB function for simulating shallow neural network

Previsione di serie temporali e modellazione con reti neurali superficiali
Eseguire una previsione di una serie temporale utilizzando l’app Neural Net Time Series e le funzioni della riga di comando.
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.
Set di dati campione per reti neurali superficiali
Elenco di set di dati campione da utilizzare negli esperimenti con reti neurali superficiali.
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.