Neural Networks Products
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Neural Networks
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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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