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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)求解时序问题

App

神经网络时序Solve a nonlinear time series problem by training a dynamic neural network

函数

nnstartNeural network getting started GUI
viewView shallow neural network
timedelaynetTime delay neural network
narxnetNonlinear autoregressive neural network with external input
narnetNonlinear autoregressive neural network
layrecnetLayer recurrent neural network
distdelaynetDistributed delay network
trainTrain shallow neural network
gensimGenerate Simulink block for shallow neural network simulation
adddelayAdd delay to neural network response
removedelayRemove delay to neural network’s response
closeloopConvert neural network open-loop feedback to closed loop
openloopConvert neural network closed-loop feedback to open loop
ploterrhistPlot error histogram
plotinerrcorrPlot input to error time-series cross-correlation
plotregressionPlot linear regression
plotresponsePlot dynamic network time series response
ploterrcorrPlot autocorrelation of error time series
genFunctionGenerate MATLAB function for simulating shallow neural network

示例和操作指南

基本设计

浅层神经网络时序预测和建模

使用 Neural Network Time Series 和命令行函数进行时序预测。

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.

设计时序 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。

训练可扩展性和效率

使用并行和 GPU 计算的神经网络

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.

Optimize Neural Network Training Speed and Memory

Make neural network training more efficient.

最优解

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.

概念

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.