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

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

**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.

**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.

**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.

**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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