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Search
Neural Networks
Neural Networks
Features
Examples
Table of Contents
Q&A
Buy Online
Table of Contents
1. Introduction
Features of This Package
2. Neural Network Theory--A Short Tutorial
Introduction to Neural Networks
Function Approximation
Time Series and Dynamic Systems
Classification and Clustering
Data Preprocessing
Linear Models
The Perceptron
Feedforward and Radial Basis Function Networks
Feedforward Neural Networks
Radial Basis Function Networks
Training Feedforward and Radial Basis Function Networks
Dynamic Neural Networks
Hopfield Network
Unsupervised and Vector Quantization (VQ) Networks
Further Reading
3. Getting Started and Basic Examples
Palettes and Loading the Package
Palettes
Loading the Package and Data
Package Conventions
Data Format
Function Names
Network Format
NetClassificationPlot
Basic Examples
Classification Problem Example
Function Approximation Example
4. The Perceptron
Perceptron Network Functions and Options
InitializePerceptron
PerceptronFit
NetInformation
NetPlot
Examples
Two Classes in Two Dimensions
Several Classes in Two Dimensions
Higher-Dimensional Classification
Further Reading
5. The Feedforward Neural Network
Feedforward Network Functions and Options
InitializeFeedForwardNet
NeuralFit
NetInformation
NetPlot
LinearizeNet
and
NeuronDelete
SetNeuralD
,
NeuralD
, and
NNModelInfo
Examples
Function Approximation in One Dimension
Function Approximation from One to Two Dimensions
Function Approximation in Two Dimensions
Classification with Feedforward Networks
Further Reading
6. The Radial Basis Function (RBF) Network
RBF Network Functions and Options
InitializeRBFNet
NeuralFit
NetInformation
NetPlot
LinearizeNet
and
NeuronDelete
SetNeuralD
,
NeuralD
, and
NNModelInfo
Examples
Function Approximation in One Dimension
Function Approximation from One to Two Dimensions
Function Approximation in Two Dimensions
Classification with RBF Networks
Further Reading
7. Training Feedforward and Radial Basis Function Networks
NeuralFit
Examples of Different Training Algorithms
Train with
FindMinimum
Troubleshooting
Regularization and Stopped Search
Regularization
Stopped Search
Example
Separable Training
Small Example
Larger Example
Options Controlling Training Results Presentation
The Training Record
Writing Your Own Training Algorithms
Further Reading
8. Dynamic Neural Networks
Dynamic Network Functions and Options
Initializing and Training Dynamic Neural Networks
NetInformation
Predicting and Simulating
Linearizing a Nonlinear Model
NetPlot
--Evaluate Model and Training
MakeRegressor
Examples
Identifying the Dynamics of a DC Motor
Identifying the Dynamics of a Hydraulic Actuator
Bias-Variance Tradeoff--Avoiding Overfitting
Fix Some Parameters--More Advanced Model Structures
Further Reading
9. Hopfield Networks
Hopfield Network Functions and Options
HopfieldFit
NetInformation
HopfieldEnergy
NetPlot
Examples
Discrete-Time Two-Dimensional Example
Discrete-Time Classification of Letters
Continuous-Time Two-Dimensional Example
Continuous-Time Classification of Letters
Further Reading
10. Unsupervised Networks
Unsupervised Network Functions and Options
InitializeUnsupervisedNet
UnsupervisedNetFit
NetInformation
UnsupervisedNetDistance
,
UnUsedNeurons
, and
NeuronDelete
NetPlot
Examples without SOM
Clustering in Two-Dimensional Space
Clustering in Three-Dimensional Space
Pitfalls with Skewed Data Density and Badly Scaled Data
Examples with SOM
Mapping from Two to One Dimension
Mapping from Two Dimensions to a Ring
Adding a SOM to an Existing Unsupervised Network
Mapping from Two to Two Dimensions
Mapping from Three to One Dimension
Mapping from Three to Two Dimensions
Change Step Length and Neighbor Influence
Further Reading
11. Vector Quantization
Vector Quantization Network Functions and Options
InitializeVQ
VQFit
NetInformation
VQDistance
,
VQPerformance
,
UnUsedNeurons
, and
NeuronDelete
NetPlot
Examples
VQ in Two-Dimensional Space
VQ in Three-Dimensional Space
Overlapping Classes
Skewed Data Densities and Badly Scaled Data
Too Few Codebook Vectors
Change Step Length
Further Reading
12. Application Examples
Classification of Paper Quality
VQ Network
RBF Network
Feedforward Network
Prediction of Currency Exchange Rate
13. Changing the Neural Network Structure
Change the Parameter Values of an Existing Network
Feedforward Network
RBF Network
Unsupervised Network
Vector Quantization Network
Fixed Parameters
Select Your Own Neuron Function
The Basis Function in an RBF Network
The Neuron Function in a Feedforward Network
Accessing the Values of the Neurons
The Neurons of a Feedforward Network
The Basis Functions of an RBF Network
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