Neural network PC tools: a practical guide
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
San Diego, Calif. u.a.
Academic Press
1990
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 414 S. Ill., graph. Darst. |
| ISBN: | 0122286405 |
Internformat
MARC
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| 245 | 1 | 0 | |a Neural network PC tools |b a practical guide |c ed. by Russell C. Eberhart ... |
| 264 | 1 | |a San Diego, Calif. u.a. |b Academic Press |c 1990 | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Neural Network
PC Tools
A Practical Guide
with a Foreword by Bernard Widrow
Edited by
Russell C Eberhart and
Roy W Dobbins
The Johns Hopkins University Applied Physics Laboratory,
Laurel, Maryland
Academic Press, Inc
Harcourt Brace /ovanovich, Publishers
San Diego New York Boston London Sydney Tokyo Toronto
CONTENTS
Contributors xiii
Foreword xv
Introduction 1
1 Background and History 9
Russell C Eberhart and Roy W Dobbins
Introduction 9
Biological Basis for Neural Network Tools 10
Introduction 10
Neurons 10
Differences between Biological Structures and NNTs 11
Where Did Neural Networks Get Their Name? 13
Neural Network Development History 14
Introduction 14
The Age of Camelot 14
The Dark Age 21
The Renaissance 28
The Age of Neoconnectionism 33
2 Implementations 35
Russell C Eberhart and Roy W Dobbins
Introduction 35
The Back-Propagation Model 36
Introduction 36
Topology and Notation 37
Network Input 39
Feedforward Calculations 40
Training by Error Back-Propagation 43
Running the Back-Propagation NNT 48
The Self-Organization Model 49
Introduction 49
Topology and Notation 50
Network Initialization and Input 53
Training Calculations 54
Testing and Running 58
3 Systems Considerations 59
Russell C Eberhart and Roy W Dobbins
Introduction 59
Evaluating Problem Categories 60
viii Contents
The Big Picture 62
Developing a System Specification 63
Specifications and Models 63
Informal Specifications 64
Structured Analysis 64
Formal Specifications 67
Applying Specifications to Neural Networks 68
Choosing Effective Roles for Neural Networks 69
Introduction 69
Network Incarnations and Reincarnations 70
Avoiding Preprocessing Pitfalls 70
Neural Networks versus Expert Systems 74
Successful Application Examples 77
4 Software Tools 81
Roy W Dobbins and Russell C Eberhart
Introduction 81
What Is Neural Network Software? 81
The Last of the Programmers? 82
Implementing Neural Networks on the PC 82
Using C and Assembly Language 83
Back-Propagation Networks 83
The Three Rs 84
Iterations Kernel, Brain, or Engine? 84
Forward and Backward 85
Computing Activations 85
Vector and Matrix Operations 87
Storage Allocation 87
Propagating Error Signals 88
Adapting Weights 89
Kohonen Self-Organizing Networks 90
Finding the Winning Unit 91
What Is This Good For? 91
Nondeterministic Response 92
Running Neural Networks 92
Getting Data into and out of the Network 93
Reading Input Patterns 93
Dealing with the Real World 94
Normalizing Data 95
Weights 95
Setting Attributes 97
Average Sum-Squared Error 98
What s It Doing? 98
Implementation Issues 101
Interpretation versus Compilation 101
Optimizing the Code How to Make the Network Scream! 102
Memory Limitations 103
Do You Really Need Floating Point? 107
Making the Most of Coprocessors 107
Debugging Networks 108
Contents ix
5 Development Environments 111
Roy W Dobbins and Russell C Eberhart
Introduction 111
What Is a Neural Network Development Environment? 112
Desirable Characteristics of Development Environments 113
Why a Development Environment? 115
Introduction to Network Modeling Languages 117
Specification Languages 118
Parallel Processing and Object-Oriented Languages 118
Conventional Programming Languages 119
A Brief Survey of Neural Network Modeling Languages 119
Specifying Neural Network Models 120
Specifying Network Architecture 122
Activation Functions 123
Learning Rules 123
Specifying the Environment 123
Update Rules 123
Neural Network Paradigms 124
A Brief Survey of Neural Network Development
Environments 124
CaseNet: A Neural Network Development Environment 126
Anatomy of CaseNet 126
CaseNet Components 127
Graphical Network Editor 128
Network Parser 129
Network Analyzer 131
Network Code Generator 133
Network Compiler 135
6 Hardware Implementations 137
D Gilbert Lee, Jr
The Transputer 138
Interprocess Communications 140
Multitasking 141
Programming Languages 142
Optimizing the Matrix-Vector Multiply 142
Using Transputers in Parallel 147
Processor Farms 147
Pipelining 148
Programming the Transputers 149
Discussion 153
Mini Case Study: Ship Image Recognition 155
Description of Ship Image Preprocessing 155
Neural Network Training Description 156
Results 157
Summary 158
Vendors 158
Contents
7 Performance Metrics 161
Russell C Eberhart, Roy W Dobbins, and Lame V Hutton
Introduction 161
Percent Correct 162
Average Sum-Squared Error 165
Normalized Error 167
Receiver Operating Characteristic Curves 169
Recall and Precision 172
Other ROC-Related Measures 173
Chi-Square Test 174
8 Network Analysis 177
Vincent G Sigillito and Russell C Eberhart
Introduction 177
Network Analysis 178
Introduction 178
The Divide-by-Three Problem 178
Other Considerations 182
The Square-within-a-Square Problem 185
Distributions of Hidden Neurode Activity Levels 186
Analyzing Weights in Trained Networks 187
Relation Factors 187
9 Expert Networks 189
Maureen Caudill
Rule-Based Expert Systems 190
Expert Networks 197
Fuzzy Mathematics 197
Fuzzy Cognitive Maps 199
An Expert Bond-Rating Network 204
Knowledge in an Expert Network 207
Expert Network Characteristics 209
Hybrid Expert Networks 211
Explanation by Confabulation 212
Rule Extraction 212
True Hybrid Expert 213
10 Case Study I: The Detection of Electroencephalogram
Spikes 215
Russell C Eberhart and Roy W Dobbins
Introduction 215
Goals and Objectives 216
Design Process 217
System Specifications 218
Contents xi
Background 220
Data Preprocessing and Categorization 220
Test Results 229
11 Case Study II: Radar Signal Processing 235
Vincent G Sigillito and Larrie V Hutton
Introduction 235
Description of the Radar Facility 236
Operation of the System and Data Collection 236
Goals and Objectives 237
The Design Process 239
Representation 240
Choosing the Number of Hidden Nodes 241
Choosing Training and Test Sets 241
Results and Discussion 242
A Preliminary Analysis 242
The Neural Network Analysis and Results 243
Conclusions 249
12 Case Study III: Technology in Search of a Buck 251
Thomas Zaremba
Introduction 251
Markets to Watch and Markets to Trade 252
Futures Market Forecasting 255
Historical Futures Market Data 256
Sources of Market Model Data 260
Futures Market Model Description 261
Why Neural Networks? 270
Why Excel? 271
Current Status, Future Plans, and Money Made 277
13 Case Study IV: Optical Character Recognition 285
Gary Entsminger
From PCX to TXT via a Neural Network 285
Why OCR Is Such a Bear 286
Objects 290
Notes and Conclusions 292
For More Information, Consult the Following 293
14 Case Study V: Making Music 295
Russell C Eberhart and Roy W Dobbins
Introduction 295
Representing Music for Neural Network Tools 296
xii Contents
Network Configurations 298
Stochasticity, Variability, and Surprise 308
Playing Your Music with MIDI 310
Now What? 312
Glossary 313
References 321
Appendix A Batchnet Back-Propagation Source Code with
Pattern, Weight, and Run Files 329
Appendix B Self-Organizing Neural Network Tool Code with
Pattern, Run, and Demo Files 345
Appendix C Turbo Pascal Code for Optical Character Recognition
Shell 367
Appendix D Source Code for Music Composition Files 375
Appendix E Additional Resources 393
Russell C Eberhart and Roy W Dobbins
Introduction 393
Organizations and Societies 394
Conferences and Symposia 396
Journals, Magazines, and Newsletters 397
Computer Bulletin Boards 401
Computer Databases 402
Summary 403
Appendix F Transputer Matrix Multiply Code 405
Index 411
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| spelling | Neural network PC tools a practical guide ed. by Russell C. Eberhart ... San Diego, Calif. u.a. Academic Press 1990 XVI, 414 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Circuit neuronique Micro-ordinateurs Ordinateurs neuronaux Microcomputers Neural computers Neural networks (Neurobiology) Personal Computer (DE-588)4115533-6 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Mikrocomputer (DE-588)4039206-5 gnd rswk-swf Neurocomputer (DE-588)4200446-9 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Neuronales Netz (DE-588)4226127-2 s Personal Computer (DE-588)4115533-6 s DE-604 Neurocomputer (DE-588)4200446-9 s Mikrocomputer (DE-588)4039206-5 s Eberhart, Russell C. Sonstige oth HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=003516842&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Neural network PC tools a practical guide Circuit neuronique Micro-ordinateurs Ordinateurs neuronaux Microcomputers Neural computers Neural networks (Neurobiology) Personal Computer (DE-588)4115533-6 gnd Neuronales Netz (DE-588)4226127-2 gnd Mikrocomputer (DE-588)4039206-5 gnd Neurocomputer (DE-588)4200446-9 gnd |
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| title | Neural network PC tools a practical guide |
| title_auth | Neural network PC tools a practical guide |
| title_exact_search | Neural network PC tools a practical guide |
| title_full | Neural network PC tools a practical guide ed. by Russell C. Eberhart ... |
| title_fullStr | Neural network PC tools a practical guide ed. by Russell C. Eberhart ... |
| title_full_unstemmed | Neural network PC tools a practical guide ed. by Russell C. Eberhart ... |
| title_short | Neural network PC tools |
| title_sort | neural network pc tools a practical guide |
| title_sub | a practical guide |
| topic | Circuit neuronique Micro-ordinateurs Ordinateurs neuronaux Microcomputers Neural computers Neural networks (Neurobiology) Personal Computer (DE-588)4115533-6 gnd Neuronales Netz (DE-588)4226127-2 gnd Mikrocomputer (DE-588)4039206-5 gnd Neurocomputer (DE-588)4200446-9 gnd |
| topic_facet | Circuit neuronique Micro-ordinateurs Ordinateurs neuronaux Microcomputers Neural computers Neural networks (Neurobiology) Personal Computer Neuronales Netz Mikrocomputer Neurocomputer Aufsatzsammlung |
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