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函数逼近和非线性回归

创建神经网络来归纳示例输入与输出之间的非线性关系

App

神经网络拟合Fit data by training a two-layer feed-forward network

函数

nnstartNeural network getting started GUI
viewView shallow neural network
fitnetFunction fitting neural network
feedforwardnetFeedforward neural network
cascadeforwardnetCascade-forward neural network
trainTrain shallow neural network
trainlmLevenberg-Marquardt backpropagation
trainbrBayesian regularization backpropagation
trainscgScaled conjugate gradient backpropagation
trainrpResilient backpropagation
mseMean squared normalized error performance function
regressionLinear regression
ploterrhistPlot error histogram
plotfitPlot function fit
plotperformPlot network performance
plotregressionPlot linear regression
plottrainstatePlot training state values
genFunctionGenerate MATLAB function for simulating shallow neural network

示例和操作指南

基本设计

使用浅层神经网络拟合数据

训练浅层神经网络以拟合数据集。

Create, Configure, and Initialize Multilayer Shallow Neural Networks

Prepare a multilayer shallow neural network.

体脂估计

此示例说明函数拟合神经网络如何基于解剖学测量值来估计体脂率。

Train and Apply Multilayer Shallow Neural Networks

Train and use a multilayer shallow network for function approximation or pattern recognition.

Analyze Shallow Neural Network Performance After Training

Analyze network performance and adjust training process, network architecture, or data.

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.

训练可扩展性和效率

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.

最优解

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.

参考书目

Shallow Neural Networks Bibliography

Refer to additional sources of information about neural networks.

概念

Workflow for Neural Network Design

Learn the primary steps in a neural network design process.

Four Levels of Neural Network Design

Learn the different levels of using neural network functionality.

Multilayer Shallow Neural Networks and Backpropagation Training

Workflow for designing a multilayer shallow feedforward neural network for function fitting and pattern recognition.

Multilayer Shallow Neural Network Architecture

Learn the architecture of a multilayer shallow neural network.

Understanding Shallow Network Data Structures

Learn how the format of input data structures affects the simulation of networks.

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。

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.