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Systems engineering neural networks

"A complete and authoritative discussion of systems engineering and neural networks In Systems Engineering Neural Networks, a team of distinguished researchers deliver a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems...

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Bibliographic Details
Main Authors: Migliaccio, Alessandro, Iannone, Giovanni (Author)
Format: eBook
Language:English
Published: Hoboken, NJ John Wiley & Sons, Inc. 2023
Subjects:
Réseaux Neuronaux (informatique)
Computer Simulation / Http://id.loc.gov/authorities/subjects/sh85029533
Neural Networks (computer Science) / Fast
Ingénierie Des Systèmes
Computer Simulation / Fast
Systems Engineering / Http://id.loc.gov/authorities/subjects/sh85131750
Simulation / Aat
Neural Networks (computer Science) / Http://id.loc.gov/authorities/subjects/sh90001937
Systems Engineering / Fast
Simulation Par Ordinateur
Systems Engineering / Aat
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • Includes bibliographical references and index
  • CHAPTER SUMMARY 204
  • QUESTIONS 205
  • SOURCES 205
  • 8 COST FUNCTION, BACK-PROPAGATION AND OTHER ITERATIVE METHODS 206
  • WHAT IS THE DIFFERENCE BETWEEN LOSS AND COST? 209
  • TRAINING THE NEURAL NETWORK 212
  • BACK-PROPAGATION (BP) 214
  • ONE MORE THING: GRADIENT METHOD AND CONJUGATE GRADIENT METHOD 218
  • ONE MORE THING: NEWTON'S METHOD 221
  • CHAPTER SUMMARY 223
  • QUESTIONS 224
  • SOURCES 224
  • 9 CONCLUSIONS AND FUTURE DEVELOPMENTS 225
  • GLOSSARY AND INSIGHTS 233
  • PROGRAMMING LANGUAGES 82
  • ONE MORE THING: SOFTWARE ENGINEERING 94
  • CHAPTER SUMMARY 101
  • QUESTIONS 102
  • SOURCES 102
  • 5 PRACTICE MAKES PERFECT 103
  • EXAMPLE 1: COSINE FUNCTION 105
  • EXAMPLE 2: CORROSION ON A METAL STRUCTURE 112
  • EXAMPLE 3: DEFINING ROLES OF ATHLETES 127
  • EXAMPLE 4: ATHLETE'S PERFORMANCE 134
  • EXAMPLE 5: TEAM PERFORMANCE 142
  • A human-defined-system 142
  • Human Factors 143
  • The sport team as system of interest 144
  • Impact of Human Error on Sports Team Performance 145
  • EXAMPLE 6: TREND PREDICTION 156
  • EXAMPLE 7: SYMPLEX AND GAME THEORY 163
  • EXAMPLE 8: SORTING MACHINE FOR LEGO® BRICKS 168
  • Part III 174
  • 6 INPUT/OUTPUT, HIDDEN LAYER AND BIAS 174
  • INPUT/OUTPUT 175
  • HIDDEN LAYER 180
  • BIAS 184
  • FINAL REMARKS 186
  • CHAPTER SUMMARY 187
  • QUESTIONS 188
  • 7 ACTIVATION FUNCTION 189
  • TYPES OF ACTIVATION FUNCTIONS 191
  • ACTIVATION FUNCTION DERIVATIVES 194
  • ACTIVATION FUNCTIONS RESPONSE TO W AND b VARIABLES 200
  • FINAL REMARKS 202
  • ABOUT THE AUTHORS
  • ACKNOWLEDGEMENTS 7
  • HOW TO READ THIS BOOK 8
  • Part I 9
  • 1 A BRIEF INTRODUCTION 9
  • THE SYSTEMS ENGINEERING APPROACH TO ARTIFICIAL INTELLIGENCE (AI) 14
  • SOURCES 18
  • CHAPTER SUMMARY 18
  • QUESTIONS 19
  • 2 DEFINING A NEURAL NETWORK 20
  • BIOLOGICAL NETWORKS 22
  • FROM BIOLOGY TO MATHEMATICS 24
  • WE CAME A FULL CIRCLE 25
  • THE MODEL OF McCULLOCH-PITTS 25
  • THE ARTIFICIAL NEURON OF ROSENBLATT 26
  • FINAL REMARKS 33
  • SOURCES 35
  • CHAPTER SUMMARY 36
  • QUESTIONS 37
  • 3 ENGINEERING NEURAL NETWORKS 38
  • A BRIEF RECAP ON SYSTEMS ENGINEERING 40
  • THE KEYSTONE: SE4AI AND AI4SE 41
  • ENGINEERING COMPLEXITY 41
  • THE SPORT SYSTEM 45
  • ENGINEERING A SPORT CLUB 51
  • OPTIMISATION 52
  • AN EXAMPLE OF DECISION MAKING 56
  • FUTURISM AND FORESIGHT 60
  • QUALITATIVE TO QUANTITATIVE 61
  • FUZZY THINKING 64
  • IT IS ALL IN THE TOOLS 74
  • SOURCES 77
  • CHAPTER SUMMARY 77
  • QUESTIONS 78
  • Part II 79
  • 4 SYSTEMS THINKING FOR SOFTWARE DEVELOPMENT 79

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