Contents

Index

• Getting Started

• Neuron Model and Network Architectures

• Perceptrons

• Linear Filters

• Backpropagation

• Solving a Problem

Improving Results

• Architecture

Feedforward Network

Simulation (sim)

Backpropagation Algorithm

• Faster Training

Variable Learning Rate (traingda, traingdx)

Resilient Backpropagation (trainrp)

Conjugate Gradient Algorithms

Line Search Routines

Quasi-Newton Algorithms

Levenberg-Marquardt (trainlm)

Reduced Memory Levenberg-Marquardt (trainlm)

• Speed and Memory Comparison

• Improving Generalization

Index Data Division (divideind)

Random Data Division (dividerand)

Block Data Division (divideblock)

Interleaved Data Division (divideint)

Summary and Discussion of Early Stopping and Regularization

• Preprocessing and Postprocessing

Min and Max (mapminmax)

Mean and Stand. Dev. (mapstd)

Principal Component Analysis (processpca)

Processing Unknown Inputs (fixunknowns)

Representing Unknown or Don't Care Targets

Posttraining Analysis (postreg)

Sample Training Session

Limitations and Cautions

• Dynamic Networks

• Control Systems

• Radial Basis Networks

• Self-Organizing and Learning Vector Quantization Nets

• Adaptive Filters and Adaptive Training

• Applications

• Advanced Topics

• Historical Networks

• Network Object Reference

• Function Reference

• Mathematical Notation

• Blocks for the Simulink Environment

• Release Notes

Backpropagation

Faster Training

Speed and Memory Comparison

Improving Generalization

Preprocessing and Postprocessing

Sample Training Session

Limitations and Cautions

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Limitations and Cautions Introduction

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