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 engineering mindset. In the book, you’ll find a general theoretical discussion of both systems engineering and neural networks accompanied by coverage of relevant and specific topics, from deep learning fundamentals to sport business applications.
Readers will discover in-depth examples derived from many years of engineering experience, a comprehensive glossary with links to further reading, and supplementary online content. The authors have also included a variety of applications programmed in both Python 3 and Microsoft Excel.
The book provides: - A thorough introduction to neural networks, introduced as key element of complex systems - Practical discussions of systems engineering and forecasting, complexity theory and optimization and how these techniques can be used to support applications outside of the traditional AI domains - Comprehensive explorations of input and output, hidden layers, and bias in neural networks, as well as activation functions, cost functions, and back-propagation - Guidelines for software development incorporating neural networks with a systems engineering methodology
Perfect for students and professionals eager to incorporate machine learning techniques into their products and processes, Systems Engineering Neural Networks will also earn a place in the libraries of managers and researchers working in areas involving neural networks.
Table of Contents
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
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
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
