Product Overview

• Using the Toolbox and Its Documentation

  Neural Network Toolbox Applications

  Neural Network Design Steps

• Fitting a Function

• Recognizing Patterns

• Clustering Data

• Time Series Prediction

  Sample Data Sets

  Introduction

• Neuron Model

• Network Architectures

  Network Object

  Configuration

• Data Structures

• Training Styles (Adapt and Train)

  Introduction

• Multilayer Neural Network Architecture

• Collect and Prepare the Data

• Create, Configure, and Initialize the Network

• Train the Network

• Post-Training Analysis (Network Validation)

  Use the Network

  Automatic Code Generation

  Limitations and Cautions

• Introduction

  Focused Time-Delay Neural Network (timedelaynet)

  Preparing Data (preparets)

  Distributed Time-Delay Neural Network (distdelaynet)

  NARX Network (narxnet, closeloop)

  Layer-Recurrent Network (layrecnet)

  Training Custom Networks

• Multiple Sequences, Time-Series Utilities, and Error Weighting

  Introduction

• NN Predictive Control

• NARMA-L2 (Feedback Linearization) Control

• Model Reference Control

• Importing and Exporting

• Introduction

• Radial Basis Functions

• Probabilistic Neural Networks

• Generalized Regression Networks

• Introduction

• Competitive Learning

• Self-Organizing Feature Maps

• Learning Vector Quantization Networks

• Introduction

  Linear Neuron Model

• Adaptive Linear Network Architecture

  Least Mean Square Error

  LMS Algorithm (learnwh)

• Adaptive Filtering (adapt)

• Custom Networks

  Additional Toolbox Functions

• Speed and Memory Comparison for Training Multilayer Networks

• Improving Generalization

  Custom Functions

  Introduction

• Perceptron Networks

• Linear Networks

• Hopfield Network

• Summary

• Network Properties

• Subobject Properties

  Bibliography

• Mathematical Notation for Equations and Figures

• Mathematics and Code Equivalents

• Blockset

• Block Generation

  Dimensions

• Variables

  Functions

  Code Efficiency

  Argument Checking