使用 NARX 网络和时延网络进行建模和预测

使用动态神经网络（包括带反馈的网络）求解时间序列问题

App

使用动态神经网络求解非线性时间序列问题

`ntstool`	打开神经网络时间序列
`view`	查看浅层神经网络

`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 shallow neural network
`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`	绘制线性回归图
`plotresponse`	Plot dynamic network time series response
`ploterrcorr`	Plot autocorrelation of error time series
`genFunction`	Generate MATLAB function for simulating shallow neural network

浅层神经网络时间序列预测和建模
使用神经网络时间序列和命令行函数进行时间序列预测。
Design Time Series Time-Delay Neural Networks
Learn to design focused time-delay neural network (FTDNN) for time-series prediction.
多步神经网络预测
了解多步神经网络预测。
设计时间序列 NARX 反馈神经网络
创建和训练外因输入非线性自回归网络 (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.
磁悬浮建模
此示例说明 NARX（具有外部输入的非线性自回归）神经网络如何对磁悬浮动态系统建模。

使用并行和 GPU 计算的浅层神经网络
使用并行和分布式计算，可以加快神经网络训练和仿真以及处理大量数据的速度。
Automatically Save Checkpoints During Neural Network Training
Save intermediate results to protect the value of long training runs.
优化神经网络训练速度和内存
使神经网络训练更加高效。

选择神经网络输入输出处理函数
对输入和目标进行预处理，以提高训练效率。
配置浅层神经网络输入和输出
了解如何在训练前使用 configure 函数手动配置网络。
划分数据以实现最优神经网络训练
使用函数将数据分为训练集、验证集和测试集。
选择多层神经网络训练函数
不同问题类型的训练算法比较。
提高浅层神经网络泛化能力，避免过拟合
了解提高泛化能力和防止过拟合的方法。
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.
浅层神经网络的样本数据集
试验浅层神经网络时要使用的样本数据集列表。
神经网络对象属性
了解定义网络基本特征的属性。
Neural Network Subobject Properties
Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.