EBOOK_ARTIFICIAL_NEURAL_NET.PDF

Neural Network Toolbox

For Use with MATLAB ®

Howard Demuth

Mark Beale

Comput ation

Visualiz ation

Program ming

User’s Guide

Version 4

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The software described in this document is furnished under a license agreement. The software may be used

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duced in any form without prior written consent from The MathWorks, Inc .

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Printing History: June 1992 First printing

April 1993 Second printing

January 1997 Third printing

July 1997 Fourth printing

January 1998 Fifth printing Revised for Version 3 (Release 11)

September 2000 Sixth printing Revised for Version 4 (Release 12)

Neural Network Toolbox User’s Guide

Contents

Preface

Explaining Neural Networks ........................... xvi

Basic Chapters ...................................... xviii

Mathematical Notation for Equations and Figures ....... xix

BasicConcepts ....................................... xix

Language ........................................... xix

WeightMatrices ..................................... xix

LayerNotation....................................... xix

FigureandEquationExamples .......................... xx

Mathematics and Code Equivalents .................... xxi

Neural Network Design Book ......................... xxii

Acknowledgments ................................... xxiii

Related Products List ................................. xxv

Introduction

Getting Started ....................................... 1-2

BasicChapters....................................... 1-2

HelpandInstallation ................................. 1-2

Neural Network Applications .......................... 1-3

ApplicationsinthisToolbox ............................ 1-3

BusinessApplications ................................. 1-3

Aerospace ........................................... 1-3

Automotive .......................................... 1-3

Banking ............................................ 1-3

CreditCardActivityChecking .......................... 1-3

Defense ............................................. 1-4

Electronics .......................................... 1-4

Entertainment ....................................... 1-4

Financial ............................................ 1-4

Industrial ........................................... 1-4

Insurance ........................................... 1-4

Manufacturing ....................................... 1-4

Medical ............................................. 1-5

OilandGas.......................................... 1-5

Robotics............................................. 1-5

Speech .............................................. 1-5

Securities ........................................... 1-5

Telecommunications . . . . . . ............................ 1-5

Transportation ....................................... 1-5

Summary . . ......................................... 1-5

Neuron Model and Network Architectures

Neuron Model ........................................ 2-2

SimpleNeuron ....................................... 2-2

TransferFunctions.................................... 2-3

NeuronWithVectorInput.............................. 2-5

Network Architectures ................................ 2-8

ALayerofNeurons ................................... 2-8

MultipleLayersofNeurons............................ 2-11

Data Structures ...................................... 2-14

Simulation With Concurrent Inputs in a Static Network . . . . 2-14

Simulation With Sequential Inputs in a Dynamic Network . . 2-15

Simulation With Concurrent Inputs in a Dynamic Network . 2-17

Training Styles ...................................... 2-20

Contents

Incremental Training (of Adaptive and Other Networks) . . . . 2-20

BatchTraining ...................................... 2-22

Summary ............................................ 2-27

FiguresandEquations................................ 2-28

Perceptrons

Introduction .......................................... 3-2

ImportantPerceptronFunctions......................... 3-3

Neuron Model ........................................ 3-4

Perceptron Architecture ............................... 3-6

Creating a Perceptron (newp) .......................... 3-7

Simulation(sim)...................................... 3-8

Initialization(init) .................................... 3-9

Learning Rules ...................................... 3-12

Perceptron Learning Rule (learnp) .................... 3-13

Training (train) ...................................... 3-16

Limitations and Cautions ............................. 3-21

OutliersandtheNormalizedPerceptronRule ............. 3-21

Graphical User Interface ............................. 3-23

IntroductiontotheGUI ............................... 3-23

CreateaPerceptronNetwork(nntool) ................... 3-23

TrainthePerceptron ................................. 3-27

ExportPerceptronResultstoWorkspace ................. 3-29

ClearNetwork/DataWindow .......................... 3-30

ImportingfromtheCommandLine ..................... 3-30

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